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IQ.Pilot/artifacts/package_runtime/tinygrad/uop/ops.py
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IQ.Lvbs CI [bot] e2b219bcf7 IQ.Pilot Prebuilt Release @ 7a91404
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from __future__ import annotations
from typing import Any, Callable, cast, TYPE_CHECKING, Type, Sequence, Iterable, Final, Iterator
import sys, time, functools, itertools, math, operator, hashlib, os, types, pickle, pathlib, inspect, weakref, collections, struct
from dataclasses import dataclass, replace
from enum import Enum, auto
from tinygrad.uop import Ops, GroupOp
from tinygrad.dtype import ConstType, dtypes, DType, DTypeLike, truncate, least_upper_dtype, least_upper_float, Invalid, AddrSpace, strong_dtype
from tinygrad.dtype import PyConst, InvalidType, weak_dtype, bitcast
from tinygrad.device import Buffer, MultiBuffer, canonicalize_device, TinyELF
from tinygrad.helpers import ContextVar, all_int, prod, getenv, all_same, Context, partition, temp, unwrap, T, argfix, Metadata, flatten, TRACEMETA
from tinygrad.helpers import PROFILE, dedup, cdiv, cmod, floordiv, floormod, diskcache_put, to_function_name, cpu_profile, TracingKey
from tinygrad.helpers import VIZ, SPEC, CAPTURE_PROCESS_REPLAY, DISALLOW_BROADCAST, get_shape, fully_flatten, to_tuple
from tinygrad.helpers import colored, ansilen, printable, Target, is_image_shape
if TYPE_CHECKING:
from tinygrad.renderer import Estimates
class AxisType(Enum):
def __repr__(self): return str(self)
DEVICE = auto(); GLOBAL = auto(); WARP = auto(); LOCAL = auto(); WEAK = auto(); GROUP_REDUCE = auto(); REDUCE = auto(); UPCAST = auto() # noqa: E702
UNROLL = auto(); THREAD = auto(); PLACEHOLDER = auto(); LOOP = auto() # noqa: E702
@dataclass(frozen=True, order=True)
class ParamArg:
slot: int
dtype: DType
vmin_vmax: tuple[PyConst, PyConst]|None = None
multiple_of: int|None = None
name: str|None = None
addrspace: AddrSpace|None = AddrSpace.GLOBAL
axis: int|None = None
device: str|tuple[str, ...]|None = None
volatile: bool = False
def __repr__(self):
fields = (("vmin_vmax", None), ("multiple_of", None), ("name", None), ("addrspace", AddrSpace.GLOBAL), ("axis", None), ("device", None),
("volatile", False))
args = [repr(self.slot), repr(self.dtype)] + [f"{k}={v!r}" for k,default in fields if (v:=getattr(self, k)) != default]
return f"ParamArg({', '.join(args)})"
axis_letters = {AxisType.DEVICE: "d", AxisType.GLOBAL: "g", AxisType.THREAD: "t", AxisType.LOCAL: "l", AxisType.WARP: "w", AxisType.WEAK: "L",
AxisType.LOOP: "L", AxisType.UPCAST: "u", AxisType.GROUP_REDUCE: "G", AxisType.REDUCE: "R", AxisType.UNROLL: "r"}
axis_colors = {AxisType.DEVICE: "green", AxisType.GLOBAL: "blue", AxisType.THREAD: "BLUE", AxisType.LOCAL: "cyan", AxisType.WARP: "CYAN",
AxisType.WEAK: "WHITE", AxisType.LOOP: "WHITE", AxisType.UPCAST: "yellow", AxisType.GROUP_REDUCE: "RED", AxisType.REDUCE: "red",
AxisType.UNROLL: "magenta"}
# NOTE: LOCAL and GROUP_REDUCE have the same priority. the order here matters
axis_to_pos = {AxisType.DEVICE: -2, AxisType.WEAK: -1, AxisType.LOOP: -1, AxisType.THREAD: 0, AxisType.GLOBAL: 0, AxisType.WARP: 1,
AxisType.LOCAL: 2, AxisType.UPCAST: 3, AxisType.GROUP_REDUCE: 2, AxisType.REDUCE: 4, AxisType.UNROLL: 5}
range_start = {Ops.STAGE: 1, Ops.REDUCE: 1, Ops.WMMA: 3, Ops.END: 1, Ops.CALL: 1, Ops.FUNCTION: 1,
Ops.SLICE: 2, Ops.LINEAR: 0}
# https://en.wikipedia.org/wiki/Identity_element
def identity_element(op:Ops, dt:DType) -> PyConst: return dt.const({Ops.ADD:0, Ops.MUL:1, Ops.MAX:dt.min}[op])
# With True as the default, this matches the old symbolic behavior
def resolve(x:UOp|bool, default:bool=True):
if isinstance(x, bool): return x
assert x.dtype == dtypes.bool, "UOp in resolve must be bool"
# NOTE: generating the text for the exception is expensive, so we do this
return bool(sx.vmin) if (sx:=x.simplify()).vmin == sx.vmax else default
# smax/smin are replacements for max/min that preserve symbolic
def _suop(lst, uop_fxn, python_fxn):
uops, nums = partition(lst, lambda x: isinstance(x, UOp))
return ssimplify(functools.reduce(uop_fxn, uops + ([python_fxn(nums)] if nums else [])))
def smax(*lst) -> sint: return _suop(argfix(*lst), UOp.maximum, max)
def smin(*lst) -> sint: return _suop(argfix(*lst), UOp.minimum, min)
def srender(x:sint) -> str: return x.render() if isinstance(x, UOp) else str(x)
def _align_left(*shapes:tuple[sint, ...]) -> tuple[tuple[sint, ...], ...]:
max_dim = max(len(s) for s in shapes)
return tuple((1,)*(max_dim-len(s))+s for s in shapes)
def _broadcast_shape(*shapes:tuple[sint, ...]) -> tuple[sint, ...]:
if all_same(shapes): return shapes[0]
# per right-aligned dim: sizes of 1 broadcast to the others, which must all agree
ret = []
for sizes in zip(*_align_left(*shapes)):
if len(rest:=dedup([s for s in sizes if isinstance(s, UOp) or s != 1])) > 1:
raise IndexError(f"shape mismatch: objects cannot be broadcast to a single shape {shapes}")
ret.append(rest[0] if rest else 1)
return tuple(ret)
def broadcast_axes(src_shape:tuple[sint, ...], out_shape:tuple[sint, ...]) -> tuple[int, ...]:
# out axes that are added or expanded
if (nleft:=len(out_shape)-len(src_shape)) < 0: raise RuntimeError(f"cannot broadcast {src_shape} into {out_shape}")
return tuple(range(nleft)) + tuple(nleft+i for i,s in enumerate(src_shape) if resolve(s == 1, default=False) and resolve(out_shape[nleft+i] != 1))
def ssimplify(uop:sint): return uop.ssimplify() if isinstance(uop, UOp) else uop
def sym_infer(uop: UOp|int, var_vals: dict[str, int]) -> int: return uop.sym_infer(var_vals) if isinstance(uop, UOp) else uop
def range_str(u:UOp, color=False) -> str:
ret = '_'.join([str(x) if x >= 0 else "m"+str(-x) for x in u.arg[0:-1]])
return colored(ret, axis_colors[u.arg[-1]]) if color else ret
def multirange_str(rngs:Iterable[UOp], color=False, pad=None) -> str:
ret = ','.join([range_str(x, color=color) for x in sorted(rngs, key=lambda x: x.arg)])
if pad is not None: ret += " " * (pad-ansilen(ret))
return ret
def shape_to_shape_arg(arg:tuple[sint, ...]) -> UOp:
for x in arg:
if isinstance(x, UOp) and not dtypes.is_int(x.dtype): raise RuntimeError(f"shape must be int, got {x.dtype} in {arg}")
if len(arg) == 0: return UOp(Ops.STACK)
elif len(arg) == 1: return UOp.const(arg[0], dtypes.weakint)
else: return UOp(Ops.STACK, src=tuple(UOp.const(x) if isinstance(x, int) else x for x in arg))
def consumer_map_from_toposort(lst:Iterable[UOp]):
ret: dict[UOp, dict[UOp, None]] = {}
for u in lst:
ret[u] = {}
for s in u.src:
if s in ret: ret[s][u] = None
return ret
def promo_dtype(src:tuple[UOp,...]) -> DType:
dts = [x.dtype for x in src]
return dts[0] if all_same(dts) else least_upper_dtype(*dts)
def dtype_from_uop(op:Ops, src:tuple[UOp,...], arg:Any) -> DType|None:
# here are the dtype production rules, eventually this will go in UOp as a recursive property
match op:
case Ops.STORE | Ops.LINEAR | Ops.SINK | Ops.PROGRAM | Ops.SOURCE | \
Ops.END | Ops.BARRIER | Ops.GROUP | Ops.IF | Ops.ENDIF | \
Ops.TUPLE | Ops.FUNCTION | Ops.CUSTOM_FUNCTION | Ops.REWRITE_ERROR:
# always void
return dtypes.void
case Ops.CALL:
# a CALL of an opaque body is void, a CALL of an address can return a value
return dtypes.void if src[0].dtype is dtypes.void else None
case Ops.CUSTOM | Ops.CUSTOMI | Ops.PYLITERAL:
return None
case Ops.INS:
return None
case Ops.NOOP:
# NOOP can be void or carry any dtype (e.g. x.f(Ops.NOOP) or substitute base with NOOP)
return None
case Ops.INDEX:
# an image access is always float, no matter the storage dtype
# TODO: should there be a CAST so src[0].dtype just work?
if (b:=src[0]).op is Ops.PARAM and is_image_shape(b.shape): return dtypes.float
return b.dtype
case Ops.LOAD | Ops.UNSHARD | Ops.REDUCE | Ops.AFTER | Ops.RANGE | \
Ops.CONTIGUOUS | Ops.CONTIGUOUS_BACKWARD | Ops.COPY | Ops.STAGE | Ops.DETACH | \
Ops.MSTACK | Ops.MSELECT | Ops.ALLREDUCE | Ops.SPECIAL:
# pass through first
return src[0].dtype
case Ops.CMPLT | Ops.CMPNE | Ops.CMPEQ:
return dtypes.bool
case Ops.SIN | Ops.LOG2 | Ops.EXP2 | Ops.SQRT | Ops.RECIPROCAL:
return least_upper_float(src[0].dtype)
case Ops.WHERE:
if src[0].dtype != dtypes.bool: raise RuntimeError(f"where cond must be bool, got {src[0].dtype}")
return promo_dtype(src[1:])
case Ops.STACK:
if len(src) == 0: return dtypes.void
return promo_dtype(src)
case Ops.BIND:
assert src[0].dtype == src[1].dtype, f"bind dtype mismatch {src[0].dtype} != {src[1].dtype}"
return src[0].dtype
case Ops.WMMA:
# WMMA output dtype is the accumulator dtype (src[2])
return src[2].dtype
case Ops.GETTUPLE:
# GETTUPLE extracts from a TUPLE (possibly through a FUNCTION)
in_tuple = src[0].src[0] if src[0].op is Ops.FUNCTION else src[0]
return in_tuple.src[arg].dtype
case Ops.GETADDR:
return dtypes.uint64
case Ops.SHL | Ops.SHR:
if not all(dtypes.is_int(x.dtype) for x in src): raise RuntimeError(f"shift operands must be int, got {[x.dtype for x in src]}")
return src[0].dtype
case Ops.BUFFER | Ops.PARAM:
assert isinstance(arg, ParamArg), "BUFFER/PARAM must have ParamArg"
return arg.dtype
case Ops.BINARY:
return dtypes.uint8
case Ops.SLICE:
# TODO: slice just shouldn't exist
return None
case Ops.CAST | Ops.BITCAST:
assert isinstance(arg, DType), f"CAST/BITCAST arg must be DType, got {arg}"
return arg
case Ops.CONST:
# derived from the value. order matters: bool is an int subclass, ConstFloat is a float subclass
if isinstance(arg, InvalidType): return dtypes.bool # Invalid is always bool, the promo lattice bottom
if isinstance(arg, bool): return dtypes.bool
if isinstance(arg, int): return dtypes.weakint
if isinstance(arg, float): return dtypes.weakfloat
raise TypeError(f"no dtype for CONST with arg {arg}")
if op in GroupOp.Unary: return src[0].dtype
# NOTE: CMPLT, CMPNE, CMPEQ, WHERE, SHL, SHR are handled above
if op in GroupOp.Broadcastable: return promo_dtype(src)
if op in GroupOp.Movement: return src[0].dtype
raise RuntimeError(f"no dtype for {op} with arg {arg}")
class UOpMetaClass(type):
ucache:dict[tuple, weakref.ReferenceType[UOp]] = {}
def __call__(cls, op:Ops, dtype:DType|None=None, src:tuple[UOp,...]=tuple(), arg:Any=None, tag:Any=None,
metadata:tuple[Metadata,...]|None=None, _buffer:Buffer|None=None):
if dtype is None: dtype = dtype_from_uop(op, src, arg) or dtypes.void
# CONST derives its dtype by value only when the constructor omits one
# TODO: delete this once the dtype field is removed, for now it just re-implements spec.py
# an INDEX presents its access dtype, which a still-weak source matches up to weakness
if SPEC == 2 and op is not Ops.CONST and \
not any(s.base.is_invalid for s in src) and (expected_dtype:=dtype_from_uop(op, src, arg)) is not None and expected_dtype != dtype and \
not (op is Ops.INDEX and weak_dtype(expected_dtype) == weak_dtype(dtype)):
raise RuntimeError(f"bad dtype {dtype}, expected {expected_dtype} on {op}")
if (wret:=UOpMetaClass.ucache.get(key:=(op, dtype, src, arg, tag), None)) is not None and (ret:=wret()) is not None: return ret
UOpMetaClass.ucache[key] = weakref.ref(created:=super().__call__(*key))
if metadata is not None: all_metadata[created] = metadata
# NOTE: this value is set by pickle when pickling a realized tensor
if _buffer is not None:
assert op is Ops.BUFFER, f"trying to set Buffer {_buffer} for {op}"
buffers[created] = _buffer
if SPEC > 1:
from tinygrad.uop.spec import spec_full, test_pyrender
if SPEC > 2:
# SPEC=3 checks the shape
_ = created._shape
if SPEC > 3:
test_pyrender(created)
with Context(CHECK_OOB=0): fret = cast(bool|None, spec_full.rewrite(created))
if fret is not True: raise RuntimeError(f"SPEC ISSUE {fret}: {created}")
return created
# some uops map to other stuff
buffers:weakref.WeakKeyDictionary[UOp, Buffer|MultiBuffer] = weakref.WeakKeyDictionary() # this maps BUFFER/SLICE uops to their device Buffers
all_metadata:weakref.WeakKeyDictionary[UOp, tuple[Metadata, ...]] = weakref.WeakKeyDictionary() # TODO: should this be here?
# recursive_property replaces functools.cached_property in recursive UOp functions to prevent RecursionError
class recursive_property(property):
def __init__(self, fxn):
self.fxn = fxn
self.nm = "_RECURSIVE_PROPERTY_"+fxn.__name__
self.__doc__ = fxn.__doc__
def __get__(self, x:UOp|None, owner=None):
if x is None: return self
if self.nm in x.__dict__: return x.__dict__[self.nm]
for node in x.toposort(gate=lambda node: self.nm not in node.__dict__): node.__dict__[self.nm] = self.fxn(node)
return x.__dict__[self.nm]
# we import this late so we can use resolve/smax in mixins
from tinygrad.mixin.op import OpMixin
from tinygrad.mixin.rand import RandMixin
# NOTE: this should be frozen, but frozen is slower
@dataclass(eq=False, slots=True)
class UOp(RandMixin, metaclass=UOpMetaClass):
op:Ops
dtype:DType = dtypes.void
src:tuple[UOp, ...] = tuple()
arg:Any = None
tag:Any = None
def __del__(self):
if Ops is not None and self.op is Ops.BUFFER and (buffer:=buffers.get(self)) is not None: buffer.ref(-1)
try: del UOpMetaClass.ucache[(self.op, self.dtype, self.src, self.arg, self.tag)]
except AttributeError: pass
def __reduce__(self):
args = [self.op, self.dtype, self.src, self.arg, self.tag, self.metadata]
if self.op is Ops.BUFFER and self.realized is not None: args.append(self.realized)
return UOp, tuple(args)
def replace(self, **kwargs) -> UOp:
new_args = (kwargs.pop("op", self.op), kwargs.pop("dtype", self.dtype), kwargs.pop("src", self.src),
kwargs.pop("arg", self.arg), kwargs.pop("tag", self.tag))
assert len(kwargs) == 0, f"unused kwargs in replace {list(kwargs)}"
if (self.op, self.dtype, self.src, self.arg, self.tag) == new_args: return self
return UOp(*new_args)
def rtag(self, tag=True): return self.replace(tag=tag)
@property
def val(self):
assert self.op is Ops.CONST, f"val is only for CONST, got {self.op}"
return self.arg
@property
def is_invalid(self) -> bool: return self.op is Ops.CONST and self.val is Invalid
@recursive_property
def key(self) -> bytes:
return hashlib.sha256(str((self.op, self.dtype, self.arg)).encode() + b"".join([s.key for s in self.src])).digest()
def __repr__(self):
from tinygrad.uop.render import pretty_print
return pretty_print(self)
def argstr(self):
if self.op is Ops.REDUCE: return f'({", ".join(map(str, self.arg))})'
return repr(self.arg)
def tagstr(self): return f", tag={self.tag}" if self.tag is not None else ""
def f(self, op, **kwargs): return UOp(op, dtype=kwargs.pop("dtype", self.dtype), src=(self,), **kwargs)
@functools.cached_property
def backward_slice(self:UOp) -> dict[UOp, None]:
res: dict[UOp, None] = self.toposort()
res.pop(self)
return res
@property
def backward_slice_with_self(self:UOp) -> dict[UOp, None]: return {self:None, **self.backward_slice}
def op_in_backward_slice_with_self(self, *ops:Ops) -> bool:
# Check self first, then iterate backward_slice (avoids creating intermediate dict)
return self.op in ops or any(x.op in ops for x in self.backward_slice)
@recursive_property
def _bool_slice(self) -> frozenset[UOp]: return frozenset().union(*[s.bool_slice for s in self.src])
# NOTE: self is added outside the cache, a cached self-reference is a cycle the refcounter can't free
@property
def bool_slice(self) -> frozenset[UOp]: return self._bool_slice | {self} if self.dtype is dtypes.bool else self._bool_slice
def toposort(self, gate:Callable|None=None, enter_calls=True) -> dict[UOp, None]:
cache: dict[UOp, None] = {}
stack: list[tuple[UOp, bool]] = [(self, False)] # each stack entry is (node, visited_flag)
while stack:
node, visited = stack.pop()
if node in cache: continue
if not visited:
if gate is None or gate(node):
stack.append((node, True)) # push node back on stack to process after its srcs
for s in reversed(node.src if enter_calls or node.op not in {Ops.CALL, Ops.FUNCTION} else node.src[1:]):
stack.append((s, False)) # push srcs on the stack
else: cache[node] = None # second time i'm seeing this node, add it to returned toposort
return cache
def topovisit(self, visitor:Callable[[UOp], T], cache:dict[UOp, T]) -> T:
# NOTE: this shares a lot of code with toposort
stack: list[tuple[UOp, bool]] = [(self, False)]
while stack:
node, visited = stack.pop()
if node in cache: continue
if not visited:
stack.append((node, True))
for s in reversed(node.src): stack.append((s, False))
else: cache[node] = visitor(node)
return cache[self]
@functools.cached_property
def tuplize(self:UOp) -> tuple:
return (self.op.value, self.arg, self.dtype,)+tuple([x.tuplize for x in self.src])
# *** uop shape stuff ***
@recursive_property
def _shape(self) -> tuple[sint, ...]|None:
match self.op:
# late ops don't have shape
case Ops.IF | Ops.BARRIER | Ops.SINK | Ops.REWRITE_ERROR | Ops.ENDIF | Ops.GROUP | \
Ops.LINEAR | Ops.PROGRAM | Ops.SOURCE | Ops.TUPLE | Ops.FUNCTION:
return None
# a void CALL has no shape, the return value of a CALL has the shape of its dtype
case Ops.CALL:
return None if self.dtype is dtypes.void else ()
# INS shape is always scalar, vector width is in the instruction encoding
case Ops.INS:
if self.dtype is dtypes.void: return None
return ()
# special (terrible) case for RESHAPE on NOOP
case Ops.RESHAPE:
if self.src[0].op is Ops.NOOP: return self.marg
# hacks for NOOP
case Ops.NOOP:
return self.src[0]._shape if len(self.src) >= 1 else None
case Ops.GETTUPLE:
# GETTUPLE extracts from a TUPLE (possibly through a FUNCTION)
in_tuple = self.src[0].src[0] if self.src[0].op is Ops.FUNCTION else self.src[0]
assert in_tuple.op is Ops.TUPLE
inner_shape = in_tuple.src[self.arg]._shape
if inner_shape is None: return None
# if through a FUNCTION, substitute internal PARAMs in the shape with corresponding args
if self.src[0].op is Ops.FUNCTION:
return tuple(graph_rewrite(s, _pm_resolve_params, self.src[0].src[1:], walk=True) if isinstance(s, UOp) else s for s in inner_shape)
return inner_shape
case Ops.INDEX:
shp:list[sint] = []
for s in self.src[1:]: shp.extend(list(s.shape))
return tuple(shp) + self.src[0].shape[len(self.src[1:]):]
case Ops.STACK:
if len(self.src) == 0: return ()
return (len(self.src),) + self.src[0].shape
case Ops.CONST:
return ()
# some ops init the shape
case Ops.GETADDR: return ()
case Ops.BIND | Ops.RANGE | Ops.SPECIAL: return ()
case Ops.BINARY: return (len(self.arg),)
case Ops.BUFFER:
if len(self.src): return self.src[0].as_shape
return ()
case Ops.SLICE:
# HACK: SLICE is used inside kernels, so we set the shape to () if it's on an INDEX
if self.src[0].op is Ops.INDEX: return ()
return (self.arg,)
case Ops.CUSTOM | Ops.CUSTOMI:
if self.dtype is dtypes.void: return None
input_shapes = [x._shape for x in self.src if x._shape is not None]
return _broadcast_shape(*input_shapes) if input_shapes else None
case Ops.CUSTOM_FUNCTION: return None
case Ops.PYLITERAL: return None
case Ops.STAGE:
# STAGE adds the existing shape to the front, opposite of INDEX
return tuple([int(r.vmax+1) for r in self.src[1:]])+self.src[0].shape
# param has shape as the only arg
case Ops.PARAM:
return self.src[0].as_shape
# wmma output shape = accumulator shape (src[2])
case Ops.WMMA:
wmma_b = _broadcast_shape(self.src[0].shape[:-1], self.src[1].shape[:-1], self.src[2].shape[:-1])
return wmma_b + (self.src[2].shape[-1],)
# passthrough ops
case Ops.MSTACK | Ops.MSELECT | Ops.DETACH | Ops.CONTIGUOUS | Ops.CONTIGUOUS_BACKWARD | Ops.AFTER | Ops.LOAD | \
Ops.COPY | Ops.ALLREDUCE | Ops.STORE | Ops.END:
return self.src[0]._shape
case Ops.BITCAST:
ps = self.src[0]._shape
if ps is None: return None
if (output_sz:=self.dtype.itemsize) != (input_sz:=self.src[0].dtype.itemsize) and len(ps) > 0:
if isinstance(ps[-1], int) and (ps[-1]*input_sz) % output_sz: raise RuntimeError("unsupported size in bitcast")
return ps[:-1]+(ssimplify((ps[-1]*input_sz) // output_sz),)
return ps
# UNSHARD marker has no shape
case Ops.UNSHARD if len(self.src) == 0: return None
# movement ops change the shape
# NOTE: ssimplify is required because the shape needs to be canonical for broadcasting and same shape checking
if self.op in GroupOp.Movement.union({Ops.UNSHARD, Ops.REDUCE}):
ps = self.src[0]._shape
if ps is None: raise RuntimeError(f"movement op {self.op} requires shape, {self.src[0].op} doesn't have one")
match self.op:
case Ops.RESHAPE:
if not all(x >= 0 for x in self.marg): raise ValueError(f"shape can't contain negative numbers {self.marg}")
if prod(ps) != prod(self.marg): raise ValueError(f"bad reshape: {ps} -> {self.marg}")
return self.marg
case Ops.EXPAND:
return tuple(self.marg) + ps
case Ops.PERMUTE:
if sorted(self.marg) != list(range(len(ps))): raise ValueError(f"invalid permutation {self.marg} of len {len(ps)}")
return tuple(ps[i] for i in self.marg)
case Ops.PAD:
# TODO: why do i need resolve here?
if len(ps) != len(self.marg) or not all(resolve(sz>=0) and resolve(0<=o) and resolve(o+s<=sz) for s,(o,sz) in zip(ps, self.marg)):
raise ValueError(f"invalid pad {self.marg} for {ps}")
return tuple(ssimplify(sz) for _,sz in self.marg)
case Ops.SHRINK:
# TODO: why do i need resolve here?
if len(ps) != len(self.marg) or not all(resolve(0<=o) and resolve(sz>=0) and resolve(o+sz<=s) for s,(o,sz) in zip(ps, self.marg)):
raise ValueError(f"invalid shrink {self.marg} for {ps}")
return tuple(ssimplify(sz) for _,sz in self.marg)
case Ops.FLIP:
if len(ps) != len(self.marg) or not all(isinstance(x, bool) for x in self.marg): raise ValueError(f"bad flip on {ps}, {self.marg}")
return ps
case Ops.UNSHARD: return tuple(s*(int(self.src[1:][self.arg.index(a)].vmax)+1) if a in self.arg else s for a,s in enumerate(ps))
case Ops.REDUCE:
num_axes = self.arg[1]
if not isinstance(num_axes, int) or num_axes < 0 or num_axes > len(ps):
raise ValueError(f"invalid type for axis: {num_axes}")
return ps[num_axes:]
if self.op in GroupOp.Unary.union({Ops.CAST}):
assert len(self.src) == 1, "unary ops must have 1 src"
return self.src[0]._shape
# elementwise ops keep the shape the same. all inputs with shape must match
if self.op in GroupOp.Broadcastable:
input_shapes = [x._shape for x in self.src]
assert len(self.src) > 0 and all(x is not None for x in input_shapes), f"None input shape not supported for {self.op}"
if DISALLOW_BROADCAST and not all_same(input_shapes):
raise RuntimeError(f"shape mismatch at {self.op}: {input_shapes} {[x.op for x in self.src]}")
# broadcasting lives in _shape property now
return _broadcast_shape(*input_shapes)
# all Ops must be explicitly handled
raise NotImplementedError(f"no shape handling for {self.op} with {self.dtype}")
@property
def shape(self) -> tuple[sint, ...]:
if (ret:=self._shape) is None: raise RuntimeError(f"shape requested, but {self.op} doesn't have a shape")
return ret
@property
def max_shape(self) -> tuple[int, ...]: return to_max_shape(self.shape)
def max_numel(self) -> int: return prod(self.max_shape)
@property
def shard_shape(self) -> tuple[sint, ...]:
if not isinstance(self.device, tuple) or self.axis is None: return self.shape
dcount = int(self.src[1].vmax)+1 if self.op is Ops.UNSHARD else len(self.device)
return tuple(x//dcount if i == self.axis else x for i,x in enumerate(self.shape))
@property
def max_shard_shape(self) -> tuple[int, ...]: return to_max_shape(self.shard_shape)
@functools.cached_property
def ended_ranges(self) -> tuple[UOp, ...]:
if self.op in range_start: return self.src[range_start[self.op]:]
if self.op is Ops.AFTER: return tuple(flatten([x.ended_ranges for x in self.src[1:]]))
# UNSHARD ends the DEVICE range: its src is per-device index math, the device axis is carried by the axis metadata
if self.op is Ops.UNSHARD: return self.src[1:]
return ()
# determine what ranges this is in
@recursive_property
def _ranges(self) -> dict[UOp, None]:
ret: dict[UOp, None] = {}
for s in self.src: ret.update(s.ranges)
for er in self.ended_ranges:
if er.op is Ops.RANGE:
# if it's a single RANGE, we don't flow through it.
ret.pop(er, None)
else:
# if it's not a RANGE, we include all ranges in srcs.
# technically we shouldn't flow through these ranges either, but this is pre pm_add_control_flow so it's the same.
for s in er.ranges: ret.pop(s, None)
return ret
@property
def ranges(self) -> dict[UOp, None]:
if self.op is Ops.RANGE: return {self:None} | self._ranges
return self._ranges
# *** uop evaluation ***
def simplify(self, tracked=False):
if self.op is Ops.CONST: return self
if self.op is Ops.SINK and all(s.op is Ops.CONST or (s.op is Ops.STACK and len(s.src) == 0) for s in self.src): return self
# late import!
from tinygrad.uop.symbolic import symbolic, pm_fold_cast_const
with Context(TRACK_MATCH_STATS=0 if not tracked else TRACK_MATCH_STATS.value):
return graph_rewrite(self, symbolic+pm_fold_cast_const, name="simplify")
def ssimplify(self) -> UOp|ConstType: return ret.val if (ret:=self.simplify()).op is Ops.CONST else ret
def _eval(self, dtype, expected_type:Type[T]) -> T:
assert self.dtype in dtype, f"eval with wrong dtype {self}"
vmin, vmax = (simple_self:=self.simplify())._min_max
if vmin != vmax: raise ValueError(f"eval failed to be a single number, range is {vmin} to {vmax} in {simple_self.render()}")
assert isinstance(vmin, expected_type), f"vmin is wrong dtype {type(vmin)} != {expected_type}"
return vmin
def __bool__(self): return self._eval((dtypes.bool,), bool)
def __int__(self): return self._eval(dtypes.ints+(dtypes.weakint,), int)
def __float__(self): return float(self._eval(dtypes.floats+(dtypes.weakfloat,), float))
def substitute(self, dvars:dict[UOp, UOp], name:str|None=None, extra_pm:PatternMatcher|None=None, walk:bool=False, enter_calls:bool=False):
dvars = {k:v for k,v in dvars.items() if k is not v}
if len(dvars) == 0: return self
with Context(TRACK_MATCH_STATS=(0 if name is None else TRACK_MATCH_STATS.value)):
return graph_rewrite(self, (extra_pm+_substitute) if extra_pm is not None else _substitute, dvars,
bottom_up=True, walk=walk, enter_calls=enter_calls, name=name)
# NOTE: this is not called by Tensor slice (Tensor handles UOps directly), but satisfies SupportsIndex for type checking
def __index__(self): return self.__int__()
# *** uop tracing stuff ***
@recursive_property
def trace_num(self):
num = next(ucount)
uop_fields[num] = (self.op, self.dtype, tuple(s.trace_num for s in self.src), self.arg, self.tag)+((self.metadata,) if TRACEMETA>=2 else ())
return num
# *** uop syntactic sugar ***
def sink(*srcs:UOp|None, **kwargs): # pylint: disable=no-self-argument
return UOp(Ops.SINK, src=tuple([x for x in srcs if x is not None]), **kwargs)
def maketuple(*srcs:UOp): # pylint: disable=no-self-argument
return UOp(Ops.TUPLE, src=srcs)
def gettuple(self, idx:int) -> UOp:
in_tuple = self.src[0] if self.op is Ops.FUNCTION else self
assert in_tuple.op is Ops.TUPLE, f"gettuple requires FUNCTION or TUPLE source, got {self.op}"
return UOp(Ops.GETTUPLE, src=(self,), arg=idx)
def group(*srcs:UOp|None): # pylint: disable=no-self-argument
if len(srcs) == 1 and isinstance(srcs[0], UOp): return srcs[0]
return UOp(Ops.GROUP, src=tuple([x for x in srcs if x is not None]))
def index(self, *srcs:UOp|int|None, **kwargs):
new_srcs: list[UOp] = [UOp.const(x) if isinstance(x, int) else x for x in srcs if x is not None]
if len(new_srcs) == 1 and new_srcs[0].op is Ops.CONST and self.op is Ops.STACK: return self.src[new_srcs[0].val]
return UOp(Ops.INDEX, src=(self,)+tuple(new_srcs), **kwargs)
def __getitem__(self, idx):
# buffers index into INDEX UOps (scalar lookup); everything else uses the shared mixin view path
if self.addrspace in (None, AddrSpace.ALU) or self.device is not None: return super(UOp, self).__getitem__(idx)
idx = self._normalize_indices(list(argfix(idx)))
if len(slice_idx:=[i for i,x in enumerate(idx) if isinstance(x, slice)]):
# apply SHRINK for slices that aren't the full range
bounds = tuple((s.start or 0, s.stop if s.stop is not None else self.shape[i]) if isinstance(s, slice) else (0, self.shape[i])
for i, s in enumerate(idx))
src = self.shrink(bounds)
non_slice_args = [x for x in idx if not isinstance(x, slice)]
if not non_slice_args: return src # all dims are slices, no indexing needed
perm = src.permute(tuple([i for i in range(src.ndim) if i not in slice_idx] + slice_idx))
return perm.index(*non_slice_args)
return self.index(*idx)
@property
def _uop(self) -> UOp: return self
@classmethod
def _wrap_uop(cls, u:UOp) -> UOp: return u
def const_like(self, b:ConstLike, dtype:DType|None=None):
ret = UOp.const(b, dtype or self.dtype)
return ret._mop(Ops.EXPAND, arg=self._shape) if self._shape and ret._shape != self._shape else ret
def vconst_like(self, b:ConstLike, dtype:DType|None=None):
# for use after movement ops have been removed
return UOp.const(b, dtype or self.dtype).broadcast(self.max_numel())
def ufix(self, x):
if isinstance(x, UOp): return x
return UOp.const(x)
def broadcast(self, count:int):
if count == 1: return self
return UOp(Ops.STACK, src=(self,)*count)
def load(self, *src:UOp, **kwargs): return UOp(Ops.LOAD, src=(self,)+src, **kwargs)
def store(self, src:UOp|ConstType, gate:UOp|None=None, **kwargs):
srcs = (self, self.const_like(src) if not isinstance(src, UOp) else src) + ((gate,) if gate is not None else ())
return UOp(Ops.STORE, src=srcs, **kwargs)
def end(self, *src:UOp): return UOp(Ops.END, src=(self,)+src) if len(src) else self
def after(self, *src:UOp, **kwargs): return UOp(Ops.AFTER, src=(self,)+src, **kwargs) if len(src) else self
@property
def without_after(self) -> UOp: return self.src[0] if self.op is Ops.AFTER else self
def barrier(self, *src:UOp): return UOp(Ops.BARRIER, src=(self,)+src)
def ins(self, arg, **kwargs): return UOp(Ops.INS, kwargs.pop("dtype", self.dtype), kwargs.pop("src", self.src), arg, kwargs.pop("tag", self.tag))
def contract(self, *rngs:UOp):
assert all(x.arg[-1] == AxisType.UPCAST for x in rngs), "all contract ranges must be upcast"
return UOp.stack(*[self.substitute(dict(zip(rngs, [r.const_like(i) for r,i in zip(rngs, idx)])))
for idx in itertools.product(*[range(int(r.vmax)+1) for r in rngs])])
def alu(self, op, *src:UOp, **kwargs): return UOp(op, src=(self, *src), **kwargs)
@staticmethod
def const(b:ConstLike, dtype:DType|None=None):
if dtype is None or b is Invalid: dtype = dtypes.from_py(b)
if isinstance(b, UOp): return b.cast(dtype)
# NOTE: it always has to be STACK now, even if they are all the same
if isinstance(b, tuple): return UOp.stack(*[UOp.const(c, dtype) for c in b])
return UOp(Ops.CONST, dtype, arg=dtype.const(b), src=())
@staticmethod
def range(end:sint, axis_id, axis_type=AxisType.WEAK, *arg, dtype=dtypes.weakint, src=(), **kwargs):
return UOp(Ops.RANGE, src=(sint_to_uop(end, dtype),)+src, arg=(axis_id, axis_type)+arg, **kwargs)
@staticmethod
def loop(axis_id:int, *arg): return UOp(Ops.RANGE, src=(UOp(Ops.NOOP),), arg=(axis_id, AxisType.WEAK)+arg)
@staticmethod
def special(end:sint, name:str, dtype=dtypes.weakint): return UOp(Ops.SPECIAL, src=(sint_to_uop(end, dtype),), arg=name)
@staticmethod
def wmma(a:UOp, b:UOp, acc:UOp, dims:tuple[int, int, int], device:str, threads:int, tc_upcast_axes=None):
# dtype_in is stored in the arg (not derived from src[0].dtype) because bitcast rewrites change src dtypes
return UOp(Ops.WMMA, src=(a, b, acc), arg=(dims, a.dtype, device, threads, tc_upcast_axes))
def _rop(self, op:Ops, axis:tuple[int, ...]):
# NOTE: we don't allow reduce on 1s axis
axis = tuple(sorted(axis))
reduce_axis = tuple(x for x in axis if resolve(self.shape[x] != 1))
if not len(reduce_axis):
return self.reshape(tuple(s for i,s in enumerate(self.shape) if i not in axis))
# permute so reduced axes are at the front
perm = reduce_axis + tuple(i for i in range(len(self.shape)) if i not in reduce_axis)
ret = UOp(Ops.REDUCE, src=(self.permute(perm),), arg=(op, len(reduce_axis)))
return ret.reshape(tuple(s for i,s in enumerate(self.shape) if i not in axis)) if axis != reduce_axis else ret
@staticmethod
def invalid(): return UOp.const(Invalid)
def valid(self, cond):
return cond.where(self, self.const_like(Invalid))
def get_idx(self) -> UOp:
if self.op is Ops.STACK: return UOp.stack(*(x.get_idx() for x in self.src))
return self.src[1] if self.op is Ops.WHERE and self.src[2].is_invalid else self
def get_valid(self) -> UOp:
if self.op is Ops.STACK: return UOp.stack(*(x.get_valid() for x in self.src))
return self.src[0] if self.op is Ops.WHERE and self.src[2].is_invalid else UOp.const(not self.is_invalid)
def reduce(self, *src:UOp, **kwargs):
arg = kwargs.pop('arg', None)
if isinstance(arg, Ops): arg = (arg, 0)
return UOp(Ops.REDUCE, src=(self,)+src, arg=arg, **kwargs)
def bufferize(self, *args, **kwargs): return UOp(Ops.STAGE, src=(self,)+args, **kwargs)
def allreduce(self, op, device:str|tuple[str, ...]):
assert isinstance(self.device, tuple), f"allreduce must be on tuple {self.device} isn't"
return UOp(Ops.ALLREDUCE, src=(self,), arg=(op, device))
def overflows(self, dtype:DType) -> bool: return self.vmin < dtype.min or dtype.max < self.vmax
def split_uop(self:UOp, sep:Ops) -> Iterator[UOp]:
if self.op is sep:
for s in self.src: yield from s.split_uop(sep)
else: yield self
# *** multi-device helpers ***
def unshard(self, axis:int|tuple[int, ...]|None, device_range:UOp|tuple[UOp, ...]|None=None):
assert axis is not None, "multi None is no longer supported"
# an UNSHARD carries the value and one sharding range per sharded axis (arg is the tuple of sharded axes,
# sorted). the single-axis axis form defaults the range to a DEVICE range over the devices; a range need not
# be DEVICE, e.g. a LOCAL range shards a kernel tile into per-thread fragments
if isinstance(axis, int): axis = (axis,)
if device_range is None:
assert isinstance(self.device, tuple), f"multi device must be tuple, {self.device} isn't"
device_range = (UOp.range(len(self.device), -1, AxisType.DEVICE),)
if isinstance(device_range, UOp): device_range = (device_range,)
assert isinstance(device_range, tuple) and len(axis) == len(device_range) and len(set(axis)) == len(axis)
axis, device_range = map(tuple, zip(*sorted(zip(axis, device_range))))
return UOp(Ops.UNSHARD, src=(self, *device_range), arg=axis)
@property
def sharding(self) -> tuple[tuple[int, UOp], ...]:
"""(axis, RANGE) pairs this value is sharded over (the source of truth for shard bounds/counts)."""
return tuple(zip(self.arg, self.src[1:])) if self.op is Ops.UNSHARD else ()
@property
def bounds(self):
if self.axis is None: raise RuntimeError("bounds is not defined when axis is None")
dcount = int(self.src[1].vmax)+1 if self.op is Ops.UNSHARD else len(self.device)
return tuple(itertools.pairwise(itertools.accumulate([self.src[0].shape[self.axis] for _ in range(dcount)], initial=0)))
@functools.cached_property
def axis(self) -> int|None:
# COPY removes axis. TODO: add more tests for this, and consider MSELECT/MSTACK
if self.op is Ops.COPY: return None
if self.op is Ops.UNSHARD:
if len(self.arg) != 1: raise RuntimeError(f"UOp is sharded on multiple axes {self.arg}, use .sharding")
return self.arg[0]
# GETTUPLE: axis comes from the specific TUPLE element, not src[0]
if self.op is Ops.GETTUPLE:
in_tuple = self.src[0].src[0] if self.src[0].op is Ops.FUNCTION else self.src[0]
return in_tuple.src[self.arg].axis if in_tuple.op is Ops.TUPLE else None
if self.op is Ops.PARAM: return self.arg.axis
# NOTE: they all have to share an axis, we always choose [-1]. src axes are right-aligned into the output shape
if self.op in GroupOp.ALU.union({Ops.STACK}):
return axes[-1] if (axes := dedup([x.axis+len(self.shape)-len(x.shape) for x in self.src if x.axis is not None])) else None
if len(self.src) == 0: return None
src_axis = self.src[0].axis
if self.op is Ops.SHRINK and src_axis is not None and self.marg[src_axis] != (0, self.src[0].shape[src_axis]):
return None # SHRINK will remove the sharding if it's on axis
if self.op is Ops.REDUCE:
if src_axis is None: return None
if src_axis < self.arg[1]: return None
return src_axis - self.arg[1]
if self.op is Ops.RESHAPE:
if src_axis is None: return None
arg_acc:list[sint] = [ssimplify(x) for x in itertools.accumulate(self.marg, operator.mul, initial=1)]
# new_axis is the last one that preserves prod(prior to new_axis) and must not move items between shards
target = ssimplify(prod(self.src[0].shape[:src_axis]))
if target not in arg_acc: raise RuntimeError(f"reshape {self.src[0].shape} -> {self.shape} moved items between shards")
new_axis = len(arg_acc) - arg_acc[::-1].index(target) - 1
dcount = len(self.device) if isinstance(self.device, tuple) else \
int(next(u.src[1] for u in self.src[0].toposort() if u.op is Ops.UNSHARD).vmax)+1
if self.shape[new_axis] % dcount != 0: raise RuntimeError(f"reshape {self.src[0].shape} -> {self.shape} moved items between shards")
return new_axis
if self.op is Ops.PERMUTE: return self.marg.index(src_axis) if src_axis is not None else None
if self.op is Ops.EXPAND: return src_axis + len(self.marg) if src_axis is not None else None
return src_axis
def _unshard(self, axis:int) -> UOp:
bsz, dcount = self.shape[axis], len(self.device)
dnum = UOp.range(dcount, -1, AxisType.DEVICE)
return self.pad(tuple((0,0) if a != axis else (bsz*dnum, bsz*(dcount-1) - bsz*dnum) for a in range(len(self.shape))))
def _shard(self, axis:int, rng:UOp) -> UOp:
if len(self.shape) == 0: return self # scalars broadcast, no sharding needed
dcount = int(rng.vmax)+1
if self.shape[axis] % dcount != 0: raise RuntimeError(f"multi axis uneven: {self.shape[axis]=} {axis=} {dcount=}")
sz = self.shape[axis] // dcount
return self.shrink(tuple((0,s) if i != axis else (rng*sz,rng*sz+sz) for i,s in enumerate(self.shape)))
def shard(self, devices:tuple[str, ...], axis:int|None=None) -> UOp:
copied = self.copy_to_device(devices)
return copied if axis is None else copied._shard(axis, UOp.range(len(devices), -1, AxisType.DEVICE)).unshard(axis)
def copy_to_device(self, device:str|tuple[str, ...], arg=None):
assert arg is None or isinstance(self.device, tuple)
inp = self if arg is None else UOp(Ops.MSELECT, src=(self,), arg=arg)
if inp.dtype in dtypes.weaks: raise RuntimeError(f"cannot create storage for weak dtype {inp.dtype}")
return UOp(Ops.COPY, src=(inp,), arg=device)
def mselect(self, arg:int) -> UOp: return UOp(Ops.MSELECT, src=(self,), arg=arg)
def mstack(self, *srcs: UOp) -> UOp: return UOp(Ops.MSTACK, src=(self,)+srcs) if len(srcs) else self
@property
def metadata(self) -> tuple[Metadata, ...]|None: return all_metadata.get(self, None)
# *** uop movement ops ***
@property
def base(self) -> UOp:
if self.op in GroupOp.Movement: return self.src[0].base
if self.op is Ops.DETACH: return self.src[0].base # DETACH can't change base
return self
# cached property here makes external_uop_gc fail, why?
@property
def as_shape(self) -> tuple[sint, ...]:
if self.op is Ops.CONST: return (self.val,)
if self.op is not Ops.STACK: return (ssimplify(self),)
return tuple(s.val if s.op is Ops.CONST else ssimplify(s) for s in self.src)
@functools.cached_property
def marg(self):
match self.op:
case Ops.RESHAPE | Ops.EXPAND: return self.src[1].as_shape
case Ops.PAD | Ops.SHRINK: return tuple(zip(self.src[1].as_shape, self.src[2].as_shape))
case Ops.PERMUTE | Ops.FLIP: return self.arg
case _: raise RuntimeError(f"{self.op} is not a MovementOp")
def _mop(self, op:Ops, arg) -> UOp:
# early NOOP
if op is Ops.EXPAND and len(arg) == 0: return self
if op in {Ops.SHRINK, Ops.PAD} and len(arg) == 0:
assert len(self.shape) == 0, "0 len arg only valid on zero length shape"
return self
match op:
case Ops.RESHAPE | Ops.EXPAND: src_args = [arg]
case Ops.PAD | Ops.SHRINK: src_args = list(zip(*arg))
case Ops.PERMUTE | Ops.FLIP: src_args = []
case Ops.STACK:
# arg is the other srcs; all are cast to the promoted dtype, spec requires STACK srcs to match its dtype
srcs = (self,)+tuple(arg)
dtype = cast(DType, dtype_from_uop(Ops.STACK, srcs, None))
# TODO: why cast here?
return UOp(Ops.STACK, dtype, tuple(u if u.base.is_invalid else u.cast(dtype) for u in srcs))
case _: raise RuntimeError(f"{op} is not a MovementOp")
usrcs = [shape_to_shape_arg(arg) for arg in src_args]
if len(usrcs) == 0: return UOp(op, src=(self,), arg=arg)
return UOp(op, src=(self,)+UOp.sink(*usrcs).simplify().src)
# *** uop Buffer stuff ***
unique_num = itertools.count(0)
def getaddr(self, device=None) -> UOp:
if self.without_after.op not in {Ops.BUFFER, Ops.SLICE, Ops.BINARY, Ops.MSTACK, Ops.MSELECT, Ops.PARAM}: return self
return UOp(Ops.GETADDR, src=(self,), arg=device or to_tuple(self.device)[0])
@staticmethod
def new_buffer(device:str|tuple[str, ...], size:int, dtype:DType, num=None):
if dtype in dtypes.weaks: raise RuntimeError(f"cannot create storage for weak dtype {dtype}")
slot = next(UOp.unique_num) if num is None else num
return UOp(Ops.BUFFER, src=(shape_to_shape_arg((size,)),), arg=ParamArg(slot, dtype, device=device))
@staticmethod
def from_buffer(opaque:Buffer, device:str|tuple[str, ...]|None=None):
if (uop:=UOp.new_buffer(device or opaque.device, opaque.size, opaque.dtype, num=-id(opaque))) not in buffers: buffers[uop] = opaque.ref(1)
else: assert buffers[uop] is opaque
return uop
def empty_like(self, dtype:DTypeLike|None=None, device:str|tuple[str, ...]|None=None) -> UOp:
device = canonicalize_device(self.device if device is None else device)
axis = self.axis if isinstance(device, tuple) else None
ret = UOp.empty(self.shard_shape if axis is not None else self.shape, dtype=strong_dtype(self.dtype) if dtype is None else dtype, device=device)
return ret.unshard(axis) if axis is not None else ret
@staticmethod
def _frompy(x:list|tuple|bytes, dtype:DType, device:str|tuple[str, ...]|None=None) -> UOp:
device = canonicalize_device(device)
if isinstance(x, bytes): ret, data = UOp.new_buffer("PYTHON", len(x)//dtype.itemsize, dtype), x
else:
# bfloat16 and fp8 have no struct format, so pack a float32 buffer and cast
bdtype = dtypes.float32 if dtype in [dtypes.bfloat16, *dtypes.fp8s] else dtype
assert bdtype.fmt is not None, f"{bdtype=} has None fmt"
ret = UOp.empty(shape:=get_shape(x), dtype=bdtype, device="PYTHON")
data = struct.pack(f"{prod(shape)}{bdtype.fmt}", *[truncate[bdtype](bdtype.const(xi)) for xi in fully_flatten(x)])
ret.buffer.allocate(memoryview(bytearray(data))) # fake realize. buffer storage must be writable, and bytes isn't
if ret.dtype != dtype: ret = ret.cast(dtype)
return ret if ret.device == device else ret.copy_to_device(device)
def clone(self, device=None) -> UOp:
device = device or self.device
ret = self.empty_like(device=device)
src = self if self.device is None or self.device == device else self.copy_to_device(device)
return ret.after(ret.store(src.cast(ret.dtype)))
@recursive_property
def device(self) -> str|tuple[str, ...]|None:
if self.op is Ops.PARAM: return self.arg.device
if self.op is Ops.STAGE: return self.arg.device
if self.op is Ops.AFTER: return self.src[0].device
if self.op is Ops.MSELECT:
assert isinstance(self.src[0].device, tuple), f"mselect must be on tuple device, getting {self.src[0].device}"
return self.src[0].device[self.arg]
if self.op is Ops.MSTACK: return tuple(cast(str, x.device) for x in self.src)
if self.op is Ops.BUFFER: return self.arg.device
if self.op is Ops.COPY: return self.arg
if self.op is Ops.ALLREDUCE: return self.arg[1]
for x in self.src:
if x.device is not None: return x.device
return None
@property
def is_virtual(self) -> bool:
# NOTE: no device means no place to store, weak means no width to store. neither can back a buffer as-is
# TODO: unify with has_buffer_identity
return self.device is None or self.dtype in dtypes.weaks
@recursive_property
def addrspace(self) -> AddrSpace|None:
if self.op is Ops.PARAM: return self.arg.addrspace
if self.op is Ops.BUFFER: return self.arg.addrspace
if self.op in {Ops.SPECIAL, Ops.RANGE}: return AddrSpace.ALU
if self.op is Ops.LOAD: return AddrSpace.ALU # LOAD brings things into the ALU
if self.op in {Ops.INDEX, Ops.CAST, Ops.AFTER, Ops.REDUCE, Ops.STORE, Ops.MSTACK, Ops.MSELECT, Ops.END, Ops.UNSHARD}:
return self.src[0].addrspace
if self.op in GroupOp.Movement: return self.src[0].addrspace
if self.op in {Ops.STACK, Ops.WMMA, Ops.GROUP} or self.op in GroupOp.Elementwise:
ad = [x.addrspace for x in self.src if x.addrspace is not None]
if not len(ad) or not all_same(ad): return None
return ad[0]
return None
@property
def buf_uop(self) -> UOp:
if self.op in {Ops.BUFFER, Ops.PARAM}: return self
if self.op is Ops.MSELECT: return self.src[0].buf_uop.mselect(self.arg)
if self.op is Ops.MSTACK: return UOp(Ops.MSTACK, src=tuple(x.buf_uop for x in self.src))
if self.base.op is Ops.AFTER: return self.base.src[0].buf_uop.base
s = self
while len(s.src) and s.op not in {Ops.BUFFER, Ops.PARAM, Ops.STAGE, Ops.MSTACK}: s = s.src[0]
return s
def contiguous_view_offset(self) -> int|None:
"""If movement ops on a BUFFER collapse to a contiguous range, return `offset` in elements. Otherwise None."""
from tinygrad.schedule.rangeify import pm_mops
from tinygrad.uop.symbolic import symbolic
# WEBGPU and CL do not support views.
# WEBGPU requires that minUniformBufferOffsetAlignment be at least 32 bytes: https://gpuweb.github.io/gpuweb/#adapter-capability-guarantees
# CL 1.1 provides the clCreateSubBuffer API, but at the time of writing, relevant CL runtimes (rusticl, adreno, nvidia, amd) do not provide
# reasonable values for CL_DEVICE_MEM_BASE_ADDR_ALIGN. cl_ext_buffer_device_address could potentially help, but this extension is not provided
# by relevant CL runtimes at time of writing.
if (dev:=self.device) is not None and any(d.startswith(("WEBGPU", "CL")) for d in ((dev,) if isinstance(dev, str) else dev)): return None
idx = self.flatten().index(UOp.range(self.numel(), 0))
out = graph_rewrite(idx, pm_mops+symbolic+pm_contiguous_view_offset, ctx=self, name="contiguous_view_offset")
return out.val if out.op is Ops.CONST and isinstance(out.val, int) else None
def has_buffer_identity(self, after_ok=False):
"""Check if this UOp has a concrete buffer identity in the graph (RESHAPE/UNSHARD -> BUFFER chain)."""
# TODO: this is confusing because UOp.variable('v', 0, 1, dtypes.weakfloat) is True for jit to work, but it doesn't have a buffer
if self.op in {Ops.RESHAPE, Ops.UNSHARD, Ops.MSELECT}: return self.src[0].has_buffer_identity(after_ok)
if after_ok and self.op == Ops.AFTER: return self.src[0].has_buffer_identity(after_ok)
return self.op in {Ops.BUFFER, Ops.SLICE, Ops.PARAM}
def _base_buffer_is_realized(self) -> bool:
"""Walk through AFTER chain to find if the underlying buffer is realized (has allocated memory)."""
u = self.base
while u.op is Ops.AFTER: u = u.src[0]
return u.is_realized
@property
def buffer(self) -> Buffer|MultiBuffer:
if self.op in {Ops.CONTIGUOUS, Ops.RESHAPE, Ops.UNSHARD, Ops.DETACH, Ops.AFTER}: return self.src[0].buffer
# this buffer can process disk tensors and simple movement ops
if self is not self.base:
buf = self.base.buffer
assert isinstance(buf, Buffer), "must be a Buffer for movement ops"
offset = self.contiguous_view_offset()
if offset is None: raise RuntimeError(f"non-contiguous view is not supported for {buf.device} buffer")
return buf.view(prod(self.max_shape), self.dtype, offset*self.dtype.itemsize)
if self.op is Ops.BITCAST:
buf = self.src[0].buffer
assert isinstance(buf, Buffer), "must be a Buffer for BITCAST"
return buf.view(prod(self.max_shape), self.dtype, 0)
if self.op is Ops.SLICE:
if (cret:=buffers.get(self)) is not None: return cret
buf = self.src[0].buffer
offset = self.src[1].val
if isinstance(buf, MultiBuffer):
mbuf = MultiBuffer.__new__(MultiBuffer)
mbuf.bufs = [b.view(self.arg, self.dtype, offset * self.src[0].dtype.itemsize) for b in buf.bufs]
buffers[self] = mbuf
return mbuf
assert isinstance(buf, Buffer), "must be a Buffer for SLICE"
buffers[self] = bv = buf.view(self.arg, self.dtype, offset * self.src[0].dtype.itemsize)
return bv
if self.op is Ops.MSELECT:
ret = self.src[0].buffer
assert isinstance(ret, MultiBuffer)
return ret.bufs[self.arg]
if self.op is Ops.MSTACK:
ret = MultiBuffer.__new__(MultiBuffer)
ret.bufs = [cast(Buffer, x.buffer) for x in self.src]
assert all_same([(x.size, x.dtype) for x in ret.bufs]), "multibuffers mismatch buffers"
return ret
assert self.op is Ops.BUFFER, f"must be BUFFER {self.op}"
if (cret:=buffers.get(self)) is not None: return cret
rdtype = self.dtype
if isinstance(self.device, tuple): ret = MultiBuffer(self.device, self.max_numel(), rdtype).ref(1)
else: ret = Buffer(self.device, self.max_numel(), rdtype).ref(1)
buffers[self] = ret
return ret
@property
def realized(self) -> Buffer|MultiBuffer|None:
if self.op is Ops.UNSHARD: return self.src[0].realized
# only these can be realized
if self.op not in (Ops.BUFFER, Ops.MSTACK): return None
# LOCAL/REG scratch buffers are never realized
if self.op is Ops.BUFFER and self.addrspace in (AddrSpace.LOCAL, AddrSpace.REG): return None
# an unbacked intermediate BUFFER (directly or as an MSTACK source) is not realized
if any(b.op is Ops.BUFFER and buffers.get(b) is None for b in self.backward_slice_with_self): return None
# NOTE: this is used by the JIT to determine which inputs we capture
return self.buffer if self.buffer.is_allocated() else None
@property
def is_realized(self) -> bool: return self.base.realized is not None
# *** uop Variable stuff ***
@staticmethod
def variable(name:str, min_val:PyConst, max_val:PyConst, dtype:DType=dtypes.weakint, multiple_of:int=1) -> UOp:
return UOp(Ops.PARAM, src=(shape_to_shape_arg(()),),
arg=ParamArg(-1, dtype, name=name, vmin_vmax=(min_val, max_val), multiple_of=multiple_of, addrspace=AddrSpace.ALU))
@property
def expr(self) -> str:
assert self.op is Ops.PARAM
return unwrap(self.arg.name)
def bind(self, val:int|UOp):
assert self.op is Ops.PARAM and self.addrspace is AddrSpace.ALU, f"op is {self.op}, need PARAM"
uval = self.const_like(val) if isinstance(val, int) else val
assert self.vmin <= uval.vmin and uval.vmax <= self.vmax, f"bind {val} not in range [{self.vmin}, {self.vmax}]"
assert uval.divides(self.arg.multiple_of) is not None, f"bind {val} not divisible by {self.arg.multiple_of}"
return UOp(Ops.BIND, src=(self, uval))
def unbind(self) -> tuple[Variable, int]:
assert self.op is Ops.BIND and self.src[0].op is Ops.PARAM and self.src[1].op is Ops.CONST, f"can't unbind {self}"
return self.src[0], self.src[1].val
def unbind_all(self) -> tuple[UOp, dict[Variable, int]]:
ret:dict[Variable, int] = {}
return graph_rewrite(self, pm_unbind, ctx=ret), ret
def variables(self) -> list[Variable]:
return sorted({x if x.op is Ops.PARAM else UOp.variable("_device_num", 0, x.vmax, dtype=x.dtype)
for x in self.backward_slice_with_self if (x.op is Ops.RANGE and x.arg[-1] is AxisType.DEVICE) or x.op is Ops.PARAM
and x.arg.addrspace is AddrSpace.ALU}, key=lambda v: v.expr)
# *** uop symbolic stuff ***
def const_factor(self) -> int:
"""largest known int that divides self"""
# TODO: for negatives it's not the largest
if self.op is Ops.CONST: return self.val
if self.op is Ops.STACK: return math.gcd(*[x.const_factor() for x in self.src])
if self.op is Ops.ADD: return math.gcd(self.src[0].const_factor(), self.src[1].const_factor())
if self.op is Ops.MUL: return self.src[0].val if self.src[0].op is Ops.CONST else self.src[1].val if self.src[1].op is Ops.CONST else 1
if self.op is Ops.PARAM and self.arg.multiple_of is not None: return self.arg.multiple_of
return 1
def divides(self, v:int) -> UOp|None:
if v==1: return self
if self.op is Ops.CONST: return self.const_like(self.val//v) if self.val%v == 0 else None
if self.op is Ops.STACK:
srcs = tuple(s.divides(v) for s in self.src)
return None if any(s is None for s in srcs) else UOp(Ops.STACK, src=cast(tuple[UOp, ...], srcs))
if self.op is Ops.ADD: return d0+d1 if (d0:=self.src[0].divides(v)) is not None and (d1:=self.src[1].divides(v)) is not None else None
if self.op is Ops.MUL:
if (d0:=self.src[0].divides(v)) is not None: return d0 * self.src[1]
if (d1:=self.src[1].divides(v)) is not None: return self.src[0] * d1
if self.op is Ops.PARAM and self.arg.multiple_of is not None: return self // v if self.arg.multiple_of%v == 0 else None
return None # generic None if we aren't sure
def pop_const(self, op=Ops.ADD) -> tuple[UOp, PyConst]: # NOTE: assume Invalid ALU is resolved
return (self.src[0], self.src[1].val) if self.op is op and self.src[1].op is Ops.CONST else (self, identity_element(op, self.dtype))
@staticmethod
def gcd(*uops: UOp) -> UOp:
terms, factors = zip(*[(u.divides(f:=u.const_factor()),f) for u in uops])
count = functools.reduce(operator.and_, [collections.Counter(term.split_uop(Ops.MUL)) for term in terms])
return math.prod([*count.elements(), terms[0].const_like(math.gcd(*factors))]) # put the const at the top
def divide_exact(self, v:UOp) -> UOp|None:
if self is v: return self.const_like(1)
if v.op is Ops.CONST: return self.divides(v.val)
if self.op is Ops.ADD: return None if (s0:=self.src[0].divide_exact(v)) is None or (s1:=self.src[1].divide_exact(v)) is None else s0+s1
if self.op is Ops.MUL:
(fac, const), (div_fac, div_const) = self.pop_const(Ops.MUL), v.pop_const(Ops.MUL)
new_count = collections.Counter(fac.split_uop(Ops.MUL))
new_count.subtract(div_fac.split_uop(Ops.MUL))
if const%div_const==0 and all(v>=0 for v in new_count.values()): return math.prod(new_count.elements(), start=self.const_like(const//div_const))
return None # generic None if we aren't sure
@property
def vmin(self) -> PyConst: return self._min_max[0]
@property
def vmax(self) -> PyConst: return self._min_max[1]
@functools.cached_property
def _min_max(self) -> tuple[PyConst, PyConst]:
if self.op in GroupOp.Binary and not dtypes.is_float(self.dtype):
(s0_vmin, s0_vmax), (s1_vmin, s1_vmax) = self.src[0]._min_max, self.src[1]._min_max
if self.op is Ops.ADD: return s0_vmin+s1_vmin, s0_vmax+s1_vmax
if self.op is Ops.SUB: return s0_vmin-s1_vmax, s0_vmax-s1_vmin
if self.op is Ops.AND and dtypes.is_int(self.dtype) and s1_vmin == s1_vmax >= 0:
return 0, s1_vmax if s0_vmin < 0 else min(s0_vmax, s1_vmax)
if self.op is Ops.MUL: return min(vals:=(s0_vmin*s1_vmin, s0_vmin*s1_vmax, s0_vmax*s1_vmin, s0_vmax*s1_vmax)), max(vals)
# SHL/SHR on consts only
if self.op is Ops.SHL and s1_vmin == s1_vmax and all_int(t:=(s0_vmin, s0_vmax, s1_vmin)): return t[0] << t[2], t[1] << t[2]
if self.op is Ops.SHR and s1_vmin == s1_vmax and all_int(t:=(s0_vmin, s0_vmax, s1_vmin)): return t[0] >> t[2], t[1] >> t[2]
if self.op is Ops.CMOD:
if (c:=s1_vmin) == s1_vmax > 0:
return (0 if s0_vmin > 0 else s0_vmin if 0 >= s0_vmin > -c else -(s1_vmax-1), 0 if s0_vmax < 0 else s0_vmax if 0 <= s0_vmax < c else c-1)
if s1_vmin > 0: return (0, s1_vmax-1) if s0_vmin >= 0 else (-(s1_vmax-1), 0) if s0_vmax <= 0 else (-(s1_vmax-1), s1_vmax-1)
if s1_vmax < 0: return (0, -s1_vmin-1) if s0_vmin >= 0 else (-(-s1_vmin-1), 0) if s0_vmax <= 0 else (-(-s1_vmin-1), -s1_vmin-1)
if self.op is Ops.CDIV:
assert isinstance(s0_vmin, int) and isinstance(s0_vmax, int) and isinstance(s1_vmin, int) and isinstance(s1_vmax, int)
if s1_vmin*s1_vmax>0:
return min(vals:=(cdiv(s0_vmin, s1_vmin), cdiv(s0_vmin, s1_vmax), cdiv(s0_vmax, s1_vmin), cdiv(s0_vmax, s1_vmax))), max(vals)
if self.op is Ops.FLOORDIV:
assert isinstance(s0_vmin, int) and isinstance(s0_vmax, int) and isinstance(s1_vmin, int) and isinstance(s1_vmax, int)
if s0_vmin > s0_vmax: return 0, 0 # numerator range is empty (e.g. RANGE with end=0)
if s1_vmin*s1_vmax>0: return min(vals:=(s0_vmin//s1_vmin, s0_vmin//s1_vmax, s0_vmax//s1_vmin, s0_vmax//s1_vmax)), max(vals)
if self.op is Ops.FLOORMOD:
assert isinstance(s0_vmin, int) and isinstance(s0_vmax, int) and isinstance(s1_vmin, int) and isinstance(s1_vmax, int)
if s0_vmin > s0_vmax: return 0, 0 # numerator range is empty (e.g. RANGE with end=0)
if (c:=s1_vmin) == s1_vmax > 0: return (s0_vmin%c, s0_vmax%c) if s0_vmin//c == s0_vmax//c else (0, c-1)
if (c:=s1_vmin) == s1_vmax < 0: return (s0_vmin%c, s0_vmax%c) if s0_vmin//c == s0_vmax//c else (c+1, 0)
if s1_vmin > 0: return (0, s1_vmax-1)
if s1_vmax < 0: return (s1_vmin+1, 0)
if self.op is Ops.XOR and s1_vmin == s1_vmax == -1 and isinstance(s0_vmin, int) and isinstance(s0_vmax, int):
return ~int(s0_vmax), ~int(s0_vmin)
if self.op is Ops.MAX: return max(s0_vmin, s1_vmin), max(s0_vmax, s1_vmax)
if self.op is Ops.CMPLT: return (s0_vmax<s1_vmin, s0_vmin<s1_vmax)
if self.op is Ops.CMPNE: return ((s0_vmax < s1_vmin) or (s1_vmax < s0_vmin), not (s0_vmin == s0_vmax == s1_vmin == s1_vmax))
if self.op is Ops.OR and self.dtype == dtypes.bool: return s0_vmin or s1_vmin, s0_vmax or s1_vmax
if self.op is Ops.AND and self.dtype == dtypes.bool: return s0_vmin and s1_vmin, s0_vmax and s1_vmax
# float has NAN issue and we use explicit NAN in transcendental
if self.op is Ops.WHERE and dtypes.is_int(self.dtype): return min(self.src[1].vmin, self.src[2].vmin), max(self.src[1].vmax, self.src[2].vmax)
# NOTE: returned UOp is assumed to be CONST
if self.op is Ops.PARAM and self.arg.vmin_vmax is not None: return self.arg.vmin_vmax
if self.op in (Ops.RANGE, Ops.SPECIAL) and self.dtype is not dtypes.void: return 0, (self.src[0]-1).vmax
if self.op is Ops.BIND: return self.src[0]._min_max # ignore the bound value
if self.op is Ops.STACK: return min(x.vmin for x in self.src), max(x.vmax for x in self.src)
if self.op is Ops.CONST and self.val is not Invalid: return self.val, self.val
if self.op is Ops.INDEX: return self.src[0]._min_max
if self.op is Ops.CAST:
# an int destination truncates a float source toward zero. trunc is monotone
smin, smax = self.src[0]._min_max
if dtypes.is_int(self.dtype) and dtypes.is_float(self.src[0].dtype) and all(math.isfinite(v) for v in (smin, smax)):
smin, smax = math.trunc(smin), math.trunc(smax)
# a cast to unsigned keeps exact bounds when the source fits
# TODO: can do more based on new dtype window
if dtypes.is_unsigned(self.dtype) and 0 <= smin and smax <= self.dtype.max: return smin, smax
if self.dtype in dtypes.floats+dtypes.sints+(dtypes.weakint,): return max(self.dtype.min, smin), min(smax, self.dtype.max)
return self.dtype.min, self.dtype.max
@functools.cached_property
def _sym_fxn(self):
from tinygrad.uop.render import _render_with_splits, renderer_infer
sself = self.simplify()
varnames = tuple(dedup(x.expr for x in sself.toposort() if x.op is Ops.PARAM and x.arg.addrspace == AddrSpace.ALU))
# TODO: sanitize varnames, or don't use naked eval while staying fast
ret = _render_with_splits(list(sself.toposort()), renderer_infer, {sself})
lines = [f" {k}={v}" for k,v in ret.items() if k != "ast"] + [f" return {ret['ast']}"]
ns: dict[str, Any] = {"max": max, "cdiv": cdiv, "cmod": cmod, "floordiv": floordiv, "floormod": floormod, "bitcast": bitcast, "dtypes": dtypes}
exec(f"def _f({','.join(varnames)}):\n"+'\n'.join(lines), ns) # pylint: disable=exec-used
return ns["_f"], varnames
def sym_infer(self, var_vals:dict[str, int]):
fxn, varnames = self._sym_fxn
return fxn(**{k:v for k,v in var_vals.items() if k in varnames})
def render(self, simplify=True, pm:PatternMatcher|None=None) -> str:
ctx: dict[UOp, str] = {}
from tinygrad.uop.render import renderer
pm = renderer if pm is None else pm
for u in (s:=self.simplify() if simplify else self).toposort():
ctx[u] = cast(str, pm.rewrite(u, ctx=ctx))
return ctx[s]
def pyrender(self):
from tinygrad.uop.render import pyrender
return pyrender(self)
# *** uop high level syntactic sugar ***
@staticmethod
def placeholder(shape:tuple[int, ...], dtype:DType, slot:int, addrspace=AddrSpace.GLOBAL, device=None, volatile=False):
dtype = strong_dtype(dtype) # storage is never weak: a placeholder commits the width of what's put in it
if addrspace is AddrSpace.GLOBAL:
ret = UOp(Ops.PARAM, src=(shape_to_shape_arg((prod(shape),)),), arg=ParamArg(slot, dtype, addrspace=addrspace, device=device,volatile=volatile))
else:
assert addrspace in (AddrSpace.LOCAL, AddrSpace.REG)
assert device is None, "LOCAL and REG placeholders cannot have a device"
ret = UOp(Ops.BUFFER, src=(shape_to_shape_arg((prod(shape),)),), arg=ParamArg(slot, dtype, addrspace=addrspace))
if len(shape) > 1: ret = ret.reshape(shape)
return ret
def placeholder_like(self, slot:int, addrspace=AddrSpace.GLOBAL):
assert all_int(self.shape), "no placeholder-like on symbolic shape"
return UOp.placeholder(self.max_shard_shape, self.dtype, slot, addrspace)
# set is store+end+after
def set(self:UOp, val:UOp|ConstType, end:UOp|tuple[UOp, ...]|list[UOp]=()) -> UOp:
return self.src[0].after(self.store(val).end(*argfix(end)))
# TODO: this should replace placeholder
@staticmethod
def param(slot:int, dtype:DType, shape:tuple[sint, ...]|None=None, device=None, vmin_vmax:tuple[PyConst, PyConst]|None=None,
multiple_of:int|None=None, name=None, addrspace=AddrSpace.GLOBAL, axis:int|None=None, volatile:bool=False):
if dtype in dtypes.weaks: raise RuntimeError(f"cannot create param for weak dtype {dtype}")
if shape is not None and axis is not None and isinstance(device, tuple):
shape = tuple(s*len(device) if i == axis else s for i,s in enumerate(shape))
src: tuple[UOp, ...] = (UOp(Ops.NOOP) if shape is None else shape_to_shape_arg(shape),)
return UOp(Ops.PARAM, src=src, arg=ParamArg(slot, dtype, vmin_vmax, multiple_of, name, addrspace, axis, device, volatile))
def param_like(self, slot:int):
if self.op is Ops.BIND: return self.src[0].replace(arg=replace(self.src[0].arg, slot=slot, name=f"p{slot}"))
addrspace = self.addrspace if self.addrspace is not None else AddrSpace.GLOBAL
return UOp.param(slot, self.dtype, self.shard_shape if self.axis is not None else self._shape, self.device, addrspace=addrspace, axis=self.axis)
@staticmethod
def custom_function(name:str, *src:UOp) -> UOp: return UOp(Ops.CUSTOM_FUNCTION, src=src, arg=name)
# opaque bodies stay as Ops.CALL; value-producing bodies become Ops.FUNCTION (wrapped in TUPLE)
_OPAQUE_CALL_BODIES = {Ops.SINK, Ops.PROGRAM, Ops.LINEAR, Ops.COPY, Ops.SLICE, Ops.CUSTOM_FUNCTION}
def call(self, *srcs:UOp, ret_dtype:DType|None=None, grad_fxn:Callable|None=None,
name:str|None=None, precompile:bool=False, precompile_backward:bool=False, aux:Any=None) -> UOp:
if ret_dtype is not None: return UOp(Ops.CALL, ret_dtype, src=(self,)+srcs)
# calls are launched per device, so an open DEVICE range is allowed to cross the call boundary
assert all(r.arg[-1] is AxisType.DEVICE for r in self.ranges), \
f"ranges {self.ranges} are leaking out of the call in {self.pyrender()}"
if self.op in UOp._OPAQUE_CALL_BODIES:
return UOp(Ops.CALL, src=(self,)+srcs, arg=CallInfo(grad_fxn, name, precompile, precompile_backward, aux))
# value-producing bodies are always wrapped in TUPLE so FUNCTION dtype is always void
body = self if self.op is Ops.TUPLE else UOp.maketuple(self)
return UOp(Ops.FUNCTION, src=(body,)+srcs, arg=CallInfo(grad_fxn, name, precompile, precompile_backward, aux))
def custom_kernel(*srcs:UOp, fxn:Callable, grad_fxn:Callable|None=None) -> list[UOp]:
placeholders = [UOp.placeholder_like(s, slot=i) for i,s in enumerate(srcs)]
kernel = fxn(*placeholders).call(*srcs, grad_fxn=grad_fxn)
return [s.after(kernel) for s in srcs]
def to_elf(self) -> TinyELF:
assert self.op is Ops.PROGRAM and isinstance(self.arg, ProgramInfo), "to_elf should only be called on a PROGRAM ast"
sig = tuple((u.arg.name, u.arg.slot, u.dtype, u._shape)
for u in tuple(filter(lambda u: u.op is Ops.PARAM and u.addrspace != AddrSpace.ALU, self.src[1].src)) + self.arg.vars)
return TinyELF(self.src[3].arg, self.arg.function_name, self.arg.target, sig)
@dataclass(frozen=True)
class KernelInfo:
name: str = "test" # name of the kernel
axis_types: tuple[AxisType, ...] = tuple()
dont_use_locals: bool = False # don't use local indexing
applied_opts: tuple = tuple()
opts_to_apply: tuple|None = None
estimates: Estimates|None = None
beam: int = 0
@property
def function_name(self): return to_function_name(self.name)
@dataclass(frozen=True)
class ProgramInfo:
name: str = "test"
global_size: tuple[int|float, ...] = (1, 1, 1)
local_size: tuple[int, ...]|None = None
vars: tuple[UOp, ...] = ()
globals: tuple[int, ...] = ()
outs: tuple[int, ...] = ()
ins: tuple[int, ...] = ()
target: Target = Target()
@property
def function_name(self): return to_function_name(self.name)
@property
def runtimevars(self) -> dict[str, int]: return {v.expr: i for i, v in enumerate(self.vars) if v.expr == 'core_id'}
def launch_dims(self, var_vals:dict[str, int]) -> tuple[tuple[int, ...], tuple[int, ...]|None]:
global_size = tuple([sym_infer(sz, var_vals) for sz in self.global_size]) # type: ignore[arg-type]
local_size = tuple([sym_infer(sz, var_vals) for sz in self.local_size]) if self.local_size is not None else None
return global_size, local_size
def vals(self, var_vals:dict[str, int]) -> tuple[int|None, ...]:
try: return tuple(var_vals[k.expr] if k.expr not in self.runtimevars else None for k in self.vars)
except KeyError as e: raise RuntimeError(f"unbound Variable {e} used by {self.function_name}") from None
@staticmethod
def from_sink(sink:UOp, target:Target=Target()) -> ProgramInfo:
_vars: list[UOp] = []
_globals: list[int] = []
outs: list[int] = []
ins: list[int] = []
global_size: list[int] = [1, 1, 1]
local_size: list[int]|None = [1, 1, 1]
for u in sink.toposort():
if u.op is Ops.PARAM and u.addrspace == AddrSpace.ALU: _vars.append(u)
if u.op is Ops.PARAM and u.addrspace != AddrSpace.ALU: _globals.append(u.arg.slot)
if u.op in (Ops.STORE, Ops.LOAD):
if (idx:=u.src[0]).op in (Ops.INDEX, Ops.SHRINK) or (u.src[0].op is Ops.CAST and (idx:=u.src[0].src[0]).op is Ops.INDEX):
if (buf:=idx.src[0].buf_uop).op is Ops.PARAM: (outs if u.op is Ops.STORE else ins).append(buf.arg.slot)
if u.op is Ops.SPECIAL:
if u.arg[0] == 'i': local_size = None
special_size = local_size if u.arg[0] == 'l' else global_size
if special_size is not None: special_size[int(u.arg[-1])] = cast(int, u.src[0].ssimplify())
if u.op is Ops.PARAM and u in _vars and u.expr == 'core_id': global_size[0] = int(u.vmax) + 1
return ProgramInfo(sink.arg.name if isinstance(sink.arg, KernelInfo) else "test", tuple(global_size),
tuple(local_size) if local_size is not None else None, tuple(sorted(dedup(_vars), key=lambda v: v.arg.slot)),
tuple(sorted(dedup(_globals))), tuple(sorted(dedup(outs))), tuple(sorted(dedup(ins))), target)
@dataclass(frozen=True)
class CallInfo:
grad_fxn: Callable|None = None
name: str|None = None
precompile: bool = False
precompile_backward: bool = False
aux: Any = None
# grad_fxn can't be pickled
def __reduce__(self): return (CallInfo, (None, self.name, self.precompile, self.precompile_backward, self.aux))
def __repr__(self):
gf = id(self.grad_fxn) if self.grad_fxn else None
return f"CallInfo({gf}, {repr(self.name)}, {self.precompile}, {self.precompile_backward})"
# ******** ops in python ********
def safe_exp2(x):
try: return 2 ** x
except OverflowError: return math.inf
def safe_pow(x, y):
try: return math.nan if isinstance(p:=pow(x, y), complex) else p
except ZeroDivisionError: return math.inf
except ValueError: return math.inf if x > 0 else -math.inf
python_alu: dict[Ops, Callable] = {
Ops.LOG2: lambda x: math.log2(x) if x > 0 else -math.inf if x == 0 else math.nan, Ops.EXP2: safe_exp2,
Ops.SQRT: lambda x: math.sqrt(x) if x >= 0 else math.nan, Ops.RECIPROCAL: lambda x: 1/x if x != 0 else math.copysign(math.inf, x),
Ops.SIN: lambda x: math.sin(x) if not math.isinf(x) else math.nan, Ops.POW: safe_pow,
Ops.TRUNC: lambda x: math.trunc(x) if math.isfinite(x) else x,
Ops.NEG: operator.neg, Ops.ADD: operator.add, Ops.SUB: operator.sub, Ops.MUL: operator.mul, Ops.CMPNE: operator.ne, Ops.CMPLT: operator.lt,
Ops.XOR: operator.xor, Ops.OR: operator.or_, Ops.AND: operator.and_, Ops.SHR: operator.rshift, Ops.SHL: operator.lshift, Ops.MAX: max,
Ops.CMOD: cmod, Ops.CDIV: cdiv, Ops.FLOORDIV: floordiv, Ops.FLOORMOD: floormod,
Ops.MULACC: lambda x,y,z: (x*y)+z, Ops.WHERE: lambda x,y,z: y if x else z, Ops.CMPEQ: operator.eq}
def exec_alu(op:Ops, dtype:DType, operands, truncate_output=True):
if any(isinstance(x, tuple) for x in operands):
count = max(len(x) for x in operands if isinstance(x, tuple))
return tuple([exec_alu(op, dtype, [x[i] if isinstance(x, tuple) else x for x in operands]) for i in range(count)])
if op in GroupOp.Binary and Invalid in operands: return Invalid
alu = python_alu[op](*operands)
if truncate_output and (truncate_fxn:=truncate.get(dtype)) is not None: return truncate_fxn(alu)
return alu
# ***** pattern matcher *****
def get_location() -> tuple[str, int]:
frm = sys._getframe(1)
# skip over ops.py and anything in mixin
while frm.f_back is not None and not frm.f_back.f_code.co_filename.startswith("<frozen"):
fn = frm.f_code.co_filename.replace("\\", "/")
if not (fn.endswith("/ops.py") or "/mixin/" in fn): break
frm = frm.f_back
return frm.f_code.co_filename, frm.f_lineno
class UPat(OpMixin):
__slots__ = ("op", "match_dtype", "match_tag", "arg", "name", "src", "is_any")
def __init__(self, op:Ops|tuple[Ops, ...]|set[Ops]|None=None, dtype:DType|tuple[DType, ...]|set[DType]|None=None,
src:tuple[UPat, ...]|list[UPat]|UPat|None=None, arg:Any=None,
name:str|None=None, allow_any_len:bool=False, custom_early_reject:set[Ops]|None=None, location=None, is_any:bool=False,
tag:Any=None):
assert op is None or isinstance(op, (Ops, tuple, set)), f"op must be Ops or tuple of Ops, not {op!r}"
self.op: tuple[Ops, ...]|None = (op,) if isinstance(op, Ops) else (tuple(op) if isinstance(op, set) else op)
self.match_dtype: tuple[DType, ...]|None = (dtype,) if isinstance(dtype, DType) else (tuple(dtype) if isinstance(dtype, set) else dtype)
self.match_tag: tuple[Any, ...]|None = (tag,) if isinstance(tag, str) else (tuple(tag) if isinstance(tag, set) else tag)
self.arg, self.name, self._in_src, self.custom_early_reject = arg, name, src, custom_early_reject
self.src: Any = None
self.is_any = is_any
assert self.name != "ctx", "UPat can't be named ctx"
assert dtype is None or isinstance(dtype, DType) or all(isinstance(x, DType) for x in dtype), f"invalid dtype {dtype}"
# try all permutations if it's a list
if isinstance(src, list): self.src = list(itertools.permutations(src)) if not all_same(src) else [tuple(src)]
# only one if it's a tuple
elif isinstance(src, tuple): self.src = [src]
# repeat if it's a UPat
elif isinstance(src, UPat): self.src = [itertools.repeat(src)]
self.strict_length = not (allow_any_len or isinstance(src, UPat) or src is None)
self.required_len: int = 0 if isinstance(src, UPat) or src is None else len(src)
self.location = location or get_location()
if custom_early_reject is not None: self.early_reject = custom_early_reject
else:
upat_match = [src] if isinstance(src, UPat) else ([] if src is None else self.src[0])
self.early_reject = {pp.op[0] for pp in upat_match if pp.op is not None and len(pp.op) == 1}
@property
def dtype(self) -> DType: return self.match_dtype[0] if self.match_dtype is not None else dtypes.void
def __reduce__(self):
return UPat, (self.op, self.match_dtype, self._in_src, self.arg, self.name, not self.strict_length, self.custom_early_reject, self.location,
self.is_any, self.match_tag)
def named(self, name:str):
return UPat(self.op, self.match_dtype, self._in_src, self.arg, name, not self.strict_length, self.custom_early_reject, tag=self.match_tag)
@staticmethod
def any(*src): return UPat(src=src, is_any=True)
def or_casted(self, name:str|None=None): return UPat.any(self if name is None else self.named(name), UPat(Ops.CAST, name=name, src=(self,)))
def or_after(self, name:str|None=None):
return UPat.any(self if name is None else self.named(name), UPat(Ops.AFTER, name=name, src=(self,), allow_any_len=True))
@staticmethod
@functools.cache
def var(name:str|None=None, dtype:DType|tuple[DType, ...]|None=None): return UPat(dtype=dtype, name=name)
@staticmethod
@functools.cache
def cvar(name:str|None=None, dtype:DType|tuple[DType, ...]|None=None, arg=None): return UPat(Ops.CONST, dtype, name=name, arg=arg)
@staticmethod
def const(b:ConstType, dtype:DType|tuple[DType, ...]|None=None): return UPat(Ops.CONST, dtype=dtype, arg=b)
# lil helper
def f(self, op, **kwargs): return UPat(op, src=(self,), **kwargs)
# copied from UOp
def sink(*srcs:UPat|None, **kwargs): # pylint: disable=no-self-argument
return UPat(Ops.SINK, src=tuple([x for x in srcs if x is not None]), **kwargs)
def index(self, *srcs:UPat|None, **kwargs):
return UPat(Ops.INDEX, src=(self,)+tuple(x for x in srcs if x is not None), **kwargs)
def cast(self, dtype=None, **kwargs):
if dtype is not None and self.match_dtype == (dtype,): return self
return UPat(Ops.CAST, dtype, (self,), **kwargs)
def bitcast(self, dtype=None): return UPat(Ops.BITCAST, dtype, (self,))
def load(self, *src:UPat, **kwargs): return UPat(Ops.LOAD, src=(self,)+src, **kwargs)
def store(self, *src:UPat, **kwargs): return UPat(Ops.STORE, src=(self,)+src, **kwargs)
def reduce(self, *src:UPat, **kwargs):
arg = kwargs.pop('arg', None)
if isinstance(arg, Ops): arg = (arg, 0)
return UPat(Ops.REDUCE, self.match_dtype, src=(self,)+src, arg=arg, **kwargs)
def broadcast(self, **kwargs): return UPat(Ops.STACK, self.match_dtype, src=self, **kwargs)
def after(self, *src:UPat, **kwargs): return UPat(Ops.AFTER, self.match_dtype, (self,)+src, **kwargs)
def end(self, *src:UPat, **kwargs): return UPat(Ops.END, src=(self,)+src, **kwargs)
def _broadcasted(self, y, reverse=False) -> tuple[UPat, UPat]:
y = self.ufix(y)
return (y, self) if reverse else (self, y)
def ufix(self, x): return UPat.cvar(arg=x) if not isinstance(x, UPat) else x
def __floordiv__(self, x): return self._binop(Ops.FLOORDIV, x, False)
def __rfloordiv__(self, x): return self._binop(Ops.FLOORDIV, x, True)
def mod(self, x, reverse=False): return self._binop(Ops.FLOORMOD, x, reverse)
def alu(self, op:Ops, *src:UPat):
asrc = (self,)+src
return UPat(op, dtypes.bool if op in {Ops.CMPLT, Ops.CMPNE} else asrc[-1].match_dtype, list(asrc) if op in GroupOp.Commutative else asrc)
def match(self:UPat, uop:UOp, store:dict[str, UOp]) -> list[dict[str, UOp]]:
if self.is_any: return flatten([x.match(uop, store.copy()) for x in self.src[0]])
if (self.op is not None and uop.op not in self.op) or \
(self.name is not None and store.setdefault(self.name, uop) is not uop) or \
(self.match_dtype is not None and uop.dtype not in self.match_dtype and uop.dtype.scalar() not in self.match_dtype) or \
(self.arg is not None and self.arg != uop.arg) or \
(self.match_tag is not None and uop.tag not in self.match_tag) or \
(len(uop.src) < self.required_len) or \
(self.strict_length and len(uop.src) != self.required_len): return []
if self.src is None: return [store]
res: list[dict[str, UOp]] = []
for vp in self.src:
stores, new_stores = [store.copy()], []
for uu, vv in zip(uop.src, vp):
for s in stores: new_stores.extend(vv.match(uu, s))
stores, new_stores = new_stores, []
res.extend(stores)
return res
def deconstruct_function(fxn:Callable) -> tuple:
new_globals = {k:v for k,v in fxn.__globals__.items() if k in fxn.__code__.co_names}
for co in fxn.__code__.co_consts:
if isinstance(co, types.CodeType): new_globals.update({k:v for k,v in fxn.__globals__.items() if k in co.co_names})
# NOTE: optional round trip through pickle!
assert fxn.__closure__ is None, "closures are not supported in pattern matchers"
ret = fxn.__code__, new_globals, fxn.__name__, fxn.__defaults__
return pickle.loads(pickle.dumps(ret)) if getenv("TEST_PICKLE") else ret
@functools.cache
def upat_interpret(p:UPat, fxn:Callable) -> Callable:
real_fxn = types.FunctionType(*deconstruct_function(fxn))
if 'ctx' in inspect.signature(real_fxn).parameters:
def universal_match(uop, ctx):
for match in p.match(uop, {}):
if (ret:=real_fxn(ctx=ctx, **match)) is not None: return ret # pylint: disable=not-callable
return None
else:
def universal_match(uop, _):
for match in p.match(uop, {}):
if (ret:=real_fxn(**match)) is not None: return ret # pylint: disable=not-callable
return None
return universal_match
def upat_deferred_compile(p:UPat, fxn:Callable, entry:list) -> Callable:
def lazy_compile(uop, ctx):
from tinygrad.uop.upat import upat_compile
entry[1] = upat_compile(p, fxn) or upat_interpret(p, fxn)
return entry[1](uop, ctx)
return lazy_compile
class PatternMatcher:
def __init__(self, patterns:Sequence[tuple[UPat, Callable|tuple]], compiled=bool(getenv("UPAT_COMPILE", 1))):
# if this comes from a pickle, we reconstruct the lambda functions here
self.patterns:list[tuple[UPat, Callable]] = [(p,types.FunctionType(*fxn) if isinstance(fxn, tuple) else fxn) for p,fxn in patterns]
# NOTE: use of DefaultDict here is very dangerous! all keys will live for the lifetime of the PatternMatcher!
self.pdict: dict[Ops, list[list]] = {}
# uop is required, arg is optional
for p,fxn in self.patterns:
assert p.op is not None
entry: list = [p, None, p.early_reject]
entry[1] = upat_deferred_compile(p, fxn, entry) if compiled else upat_interpret(p, fxn)
for uop in p.op: self.pdict.setdefault(uop, []).append(entry)
def __reduce__(self): return PatternMatcher, ([(x,deconstruct_function(fxn) if fxn.__name__ == "<lambda>" else fxn) for x,fxn in self.patterns],)
@functools.cache # pylint: disable=method-cache-max-size-none
def __add__(self, more:PatternMatcher) -> PatternMatcher: return PatternMatcher(self.patterns+more.patterns)
def rewrite(self, uop:UOp, ctx=None):
if len(pats:=self.pdict.get(uop.op, [])):
if (ler:=uop.__dict__.get('_src_ops')) is None: uop.__dict__['_src_ops'] = ler = {u.op for u in uop.src}
for _,match,early_reject in pats:
if not early_reject.issubset(ler): continue
if (ret:=match(uop, ctx)) is not None and ret is not uop: return ret
return None
# *** tracking pattern matcher ***
TRACK_MATCH_STATS = ContextVar("TRACK_MATCH_STATS", 2 if VIZ else 0)
REWRITE_STACK_LIMIT = ContextVar("REWRITE_STACK_LIMIT", 250000)
match_stats:dict[UPat, list[int|float]] = dict()
# TRACK_MATCH_STATS>=2 or VIZ=1 saves all matches
ucount = itertools.count()
uop_fields:dict[int, tuple] = {}
@dataclass(frozen=True)
class TrackedGraphRewrite:
loc:tuple[str, int] # location that called graph_rewrite
sink:int # the sink input to graph_rewrite
matches:list[tuple[int, int, tuple, float]] # before/after UOp, UPat location and time
name:str # name of the rewrite
depth:int # depth if it's a subrewrite
bottom_up:bool
walk:bool
enter_calls:bool
tracked_keys:list[TracingKey] = []
tracked_ctxs:list[list[TrackedGraphRewrite]] = []
_name_cnt:dict[str, itertools.count] = {}
if CAPTURE_PROCESS_REPLAY:
replay_capture: list[bytes] = []
import atexit, uuid
@atexit.register
def save_to_diskcache():
uid = uuid.uuid4() # one id per process
for i,v in enumerate(replay_capture): diskcache_put("process_replay", f"{uid}_{i}", v, prepickled=True)
def add_trace_group(kt:TracingKey) -> None:
tracked_keys.append(kt)
tracked_ctxs.append([])
active_group:list[int] = []
active_rewrites:list[TrackedGraphRewrite] = []
def rewrite_group(name:Callable[..., str|TracingKey]|bool=True, replay:bool=False, new_ctx:bool=True):
if not new_ctx: assert not callable(name) and not replay, "name fxn and replay are only supported for new_ctx groups"
def _decorator(func):
def __wrapper(*args, **kwargs):
# without tracking, we just call the function (unless top-level, which always profiles)
if TRACK_MATCH_STATS < 2 and not new_ctx: return func(*args, **kwargs)
fn = key = func.__name__
idx = -1
if TRACK_MATCH_STATS >= 2:
if new_ctx:
add_trace_group(key:=TracingKey(n:=f"{fn} n{next(_name_cnt.setdefault(fn, itertools.count(1)))}", (n,)))
active_group.append(idx:=len(tracked_keys)-1)
else:
rewrite_name = str(kwargs.get("name", None) or fn)
assert args and isinstance(args[0], UOp), f"invalid match tracing inputs for {rewrite_name} with {args}"
loc = ((frm:=sys._getframe(1)).f_code.co_filename, frm.f_lineno)
depth = len(active_rewrites)
if not tracked_ctxs: add_trace_group(TracingKey(f"default {fn}"))
dest_group = active_group[-1] if active_group else len(tracked_ctxs)-1
tracked_ctxs[dest_group].append(ctx:=TrackedGraphRewrite(loc, args[0].trace_num, [], rewrite_name, depth, kwargs.get("bottom_up", False),
kwargs.get("walk", False), kwargs.get("enter_calls", False)))
active_rewrites.append(ctx)
key = rewrite_name # profile spans are named after the rewrite step
with cpu_profile(key, "TINY") as e:
ret = func(*args, **kwargs)
if TRACK_MATCH_STATS >= 2:
if new_ctx: active_group.pop()
else: active_rewrites.pop()
if callable(name):
name_ret = name(*args, **kwargs, ret=ret)
assert isinstance(name_ret, (TracingKey, str)), f"name function returned {type(name_ret)}"
tracked_keys[idx] = k = TracingKey(n:=tracked_keys[idx].display_name.replace(fn, name_ret), (n,)) if isinstance(name_ret, str) else name_ret
e.name = TracingKey(k.display_name if isinstance(name_ret, str) else f"{fn} for {k.display_name}", k.keys)
if CAPTURE_PROCESS_REPLAY and replay:
# find the unittest frame we're capturing in
frm = sys._getframe(1)
while (f_back:=frm.f_back) is not None and "unittest" not in f_back.f_code.co_filename: frm = f_back
replay_loc = f"{frm.f_code.co_filename.split('/')[-1]}:{frm.f_lineno} {frm.f_code.co_name}"
# capture global context vars and all the args passed in
inputs = (fn, args, kwargs, ContextVar._cache)
replay_capture.append(pickle.dumps(inputs+(replay_loc, ret)))
return ret
return __wrapper
return _decorator
class TrackedPatternMatcher(PatternMatcher):
def rewrite(self, uop:UOp, ctx=None):
if len(pats:=self.pdict.get(uop.op, [])):
ret = None
ler = {u.op for u in uop.src}
for p,match,early_reject in pats:
if p not in match_stats: match_stats[p] = [0,0,0.0,0.0]
st = time.perf_counter()
if not early_reject.issubset(ler):
match_stats[p][2] += time.perf_counter()-st
continue
match_stats[p][1] += 1
try: ret = match(uop, ctx)
except Exception as e:
if TRACK_MATCH_STATS >= 2 and active_rewrites:
err_str = f"{type(e).__name__}\n{sys.exc_info()[1]}"
active_rewrites[-1].matches.append((uop.trace_num, UOp(Ops.REWRITE_ERROR, src=uop.src, arg=err_str).trace_num, p.location, 0))
raise
if ret is not None and ret is not uop:
match_stats[p][0] += 1
match_stats[p][3] += (et:=time.perf_counter()-st)
if TRACK_MATCH_STATS >= 3: print(f"{et*1e6:7.2f} us -- ", printable(p.location))
if TRACK_MATCH_STATS >= 2 and isinstance(ret, UOp) and active_rewrites:
active_rewrites[-1].matches.append((uop.trace_num, ret.trace_num, p.location, et))
return ret
match_stats[p][2] += time.perf_counter()-st
return None
@dataclass(frozen=True)
class RewriteTrace: keys:list[TracingKey]; rewrites:list[list[TrackedGraphRewrite]]; uop_fields:dict[int, tuple] # noqa: E702
if TRACK_MATCH_STATS or PROFILE:
PatternMatcher = TrackedPatternMatcher # type: ignore
import atexit
@atexit.register
def print_match_stats():
if TRACK_MATCH_STATS >= 2:
with open(fn:=temp("rewrites.pkl", append_user=True), "wb") as f:
print(f"rewrote {len(tracked_ctxs)} graphs and matched {sum(len(r.matches) for x in tracked_ctxs for r in x)} times, saved to {fn}")
pickle.dump(RewriteTrace(tracked_keys, tracked_ctxs, uop_fields), f)
TRACK_MATCH_STATS.value = 0
launch_viz("REWRITE_DATA", temp("rewrites.pkl", append_user=True))
if getenv("PRINT_MATCH_STATS", TRACK_MATCH_STATS.value and not VIZ):
ret = [0,0,0.0,0.0]
for k,v in sorted(list(match_stats.items()), key=lambda x: x[1][2]+x[1][3]):
loc_str = f"{k.location[0].split('/')[-1]}:{k.location[1]}"
if v[1] != 0: print(f"{v[0]:6d} / {v[1]:7d} -- {v[3]*1000.:9.2f} / {(v[2]+v[3])*1000.:9.2f} ms -- {loc_str:20s}", printable(k.location))
ret = [x+y for x,y in zip(ret, v)]
print(f"{ret[0]:6d} / {ret[1]:7d} -- {ret[3]*1000.:9.2f} / {(ret[2]+ret[3])*1000.:9.2f} ms -- TOTAL")
print(f"{len(match_stats)} rules, {sum(v[0] > 0 for v in match_stats.values())} matched once")
def launch_viz(env_str:str, data:str):
os.environ[f"{env_str}_DATA"] = data
if not TRACK_MATCH_STATS and not PROFILE:
os.environ["VIZ"], os.environ["PROFILE"], os.environ["TRACK_MATCH_STATS"] = "0", "0", "0"
args = ['--rewrites-path', os.getenv("REWRITE_DATA", "")] if os.getenv("REWRITE_DATA", "") else []
args += ['--profile-path', os.getenv("PROFILE_DATA", "")] if os.getenv("PROFILE_DATA", "") else []
viz_path = pathlib.Path(__file__).resolve().parent.parent / "viz" / "serve.py"
if VIZ > 0 and sys.stdout.isatty(): os.execv(sys.executable, [sys.executable, viz_path.as_posix()] + args)
if VIZ: print("saved viz files, view using: python -m tinygrad.viz.cli")
VIZ.value = 0
# *** simple graph rewrite engine ***
# A pure Python sentinel, but *typed* as UOp so it fits all the dict annotations
SENTINEL: Final[UOp] = cast(UOp, object())
class BottomUpGate(Exception): pass
class RewriteContext:
def __init__(self, pm, bpm, ctx=None, enter_calls=False):
self.pm: PatternMatcher|None = pm
self.bpm: PatternMatcher|None = bpm
self.bpm_cache: dict[UOp, UOp|None] = {}
self.ctx = ctx
self.replace: dict[UOp, UOp] = {}
self.enter_calls = enter_calls
# no cache needed: pm_rewrite is called at most once per UOp due to the replace dict check in unified_rewrite
def pm_rewrite(self, x:UOp) -> UOp|None: return unwrap(self.pm).rewrite(x, self.ctx)
def cached_bpm_rewrite(self, x:UOp) -> UOp|None:
if (ret:=self.bpm_cache.get(x,SENTINEL)) is not SENTINEL: return ret
ret = self.bpm_cache[x] = unwrap(self.bpm).rewrite(x, self.ctx)
return ret
def walk_rewrite(self, root:UOp) -> UOp:
"""MLIR-style Walk Pattern Rewrite Driver: single-pass, no re-traversal into rewritten subtrees."""
stack: list[tuple[UOp, bool]] = [(root, False)]
while stack:
n, processed = stack.pop()
if n in self.replace: continue
if not processed:
# bottom-up: try bpm on original node first, if it rewrites, use result as-is (no traversal into replacement)
if self.bpm is not None and (rewritten:=self.cached_bpm_rewrite(n)) is not None:
self.replace[n] = rewritten
continue
# no rewrite, process children then come back to rebuild
stack.append((n, True))
if not self.enter_calls and (n.op is Ops.FUNCTION or (n.op is Ops.CALL and n.src[0].op in UOp._OPAQUE_CALL_BODIES)):
self.replace[n.src[0]] = n.src[0]
for x in reversed(n.src):
if x not in self.replace: stack.append((x, False))
else:
# rebuild node with rewritten srcs
new_src = tuple(self.replace.get(x, x) for x in n.src)
new_n = UOp(n.op, _rebuild_dtype(n, new_src), new_src, n.arg, n.tag) if new_src != n.src else n
# top-down: try pm on rebuilt node, use result as-is (no re-traversal)
if self.pm is not None and (rewritten:=self.pm_rewrite(new_n)) is not None: new_n = rewritten
self.replace[n] = new_n
return self.replace.get(root, root)
def unified_rewrite(self, root:UOp) -> UOp:
stack: collections.deque[tuple[UOp, int, UOp]] = collections.deque([(root, 0, root)])
on_stack = {root} # all UOps either on the stack or in self.replace, i.e. dont have to be placed again
waitlist: dict[UOp, list[tuple[UOp, int, UOp]]] = {} # UOps waiting on a dependency to be in self.replace
while stack:
if len(stack) > REWRITE_STACK_LIMIT: raise RuntimeError("infinite loop in graph_rewrite (stack too big)")
n, stage, new_n = stack.pop()
if n in self.replace: continue # skip any nodes we have seen
if stage == 0:
# if bottom up, we rewrite this node early. in both cases, we add its srcs to the stack
if self.bpm is not None:
# apply rewrite rules until a fixed point is reached. may return `uop` itself if PatternMatcher doesn't match
test_n: UOp|None = n
seen = set()
try:
while test_n is not None:
if test_n in seen: raise RuntimeError("infinite loop in fixed_point_rewrite")
seen.add(test_n)
new_n, test_n = test_n, self.cached_bpm_rewrite(test_n)
except BottomUpGate:
# if the bpm matching raised a gate, we are done with this node and dont continue down the srcs
self.replace[n] = unwrap(test_n)
if n in waitlist: stack.extend(waitlist.pop(n))
continue
stack.append((n, 1, new_n))
# NOTE: CALL/FUNCTION are handled as a special case.
# The function that is called is not included in the graph_rewrite.
# If you want to graph_rewrite a call, you can
# A CALL of an address is not a body, its srcs are regular dataflow
if not self.enter_calls and (new_n.op is Ops.FUNCTION or (new_n.op is Ops.CALL and new_n.src[0].op in UOp._OPAQUE_CALL_BODIES)):
self.replace[new_n.src[0]] = new_n.src[0]
for x in reversed(new_n.src):
if x in on_stack: continue
stack.append((x, 0, x))
on_stack.add(x)
elif stage == 1:
tmp = []
for x in new_n.src:
if (rx:=self.replace.get(x, SENTINEL)) is SENTINEL:
# source not ready: register in waitlist instead of spinning
waitlist.setdefault(x, []).append((n, 1, new_n))
break
tmp.append(rx)
else:
# in stage 1, once all srcs are rewritten, rebuild (if changed) or run top-down rewrite
if (new_src:=tuple(tmp)) == new_n.src:
# if top down, do the rewrite. if no rewrite or bottom up, we are done rewriting this node so we add it to the dict
if self.pm is None or (new_src_n:=self.pm_rewrite(new_n)) is None:
self.replace[n] = new_n
if n in waitlist: stack.extend(waitlist.pop(n))
continue
else:
# if srcs changed from rewrites, construct a new UOp with the new srcs
new_src_n = UOp(new_n.op, _rebuild_dtype(new_n, new_src), new_src, new_n.arg, new_n.tag)
# trigger a rewrite of new_src_n, then after that rewrite is done, link it back to n
stack.append((n, 2, new_src_n))
stack.append((new_src_n, 0, new_src_n))
else:
# in stage 2, we link the result of new_n to the result of n
if (replaced_new_n:=self.replace.get(new_n, SENTINEL)) is SENTINEL:
# not ready: register in waitlist instead of spinning
waitlist.setdefault(new_n, []).append((n, 2, new_n))
else:
# otherwise we are done
self.replace[n] = replaced_new_n
if n in waitlist: stack.extend(waitlist.pop(n))
return self.replace[root]
@rewrite_group(new_ctx=False)
def graph_rewrite(sink:UOp, pm:PatternMatcher, ctx=None, bottom_up=False, name=None, bpm=None, walk=False, enter_calls=False) -> UOp:
rewrite_ctx = RewriteContext(pm if not bottom_up else None, pm if bottom_up else bpm, ctx, enter_calls)
return rewrite_ctx.walk_rewrite(sink) if walk else rewrite_ctx.unified_rewrite(sink)
def _rebuild_dtype(n:UOp, new_src:tuple[UOp,...]) -> DType:
# TODO: delete this once the dtype field is removed, every rebuild will re-derive
if all(a.dtype is b.dtype or b.base.is_invalid for a,b in zip(n.src, new_src)): return n.dtype
return dtype_from_uop(n.op, new_src, n.arg) or n.dtype
def sint_to_uop(x:sint, dtype=dtypes.weakint) -> UOp: return UOp.const(x, dtype)
def to_max_shape(shape:tuple[sint, ...]) -> tuple[int, ...]: return tuple(int(x.vmax) if isinstance(x, UOp) else x for x in shape)
_substitute = PatternMatcher([(UPat(tuple(Ops), name="x"), lambda ctx,x: ctx.get(x,None))])
_pm_resolve_params = PatternMatcher([(UPat(Ops.PARAM, name="p"), lambda ctx,p: ctx[p.arg.slot])])
remove_all_tags = PatternMatcher([(UPat(GroupOp.All, name="x"), lambda x: x.replace(tag=None) if x.tag is not None else None)])
def gate_kernel_sink(x:UOp) -> bool:
if x.op is Ops.LINEAR: return False
if x.op is Ops.SINK and isinstance(x.arg, KernelInfo): return False
return True
def do_unbind(ctx:dict[Variable, int], x:UOp):
v,i = x.unbind()
ctx[v] = i
return v
pm_unbind = PatternMatcher([(UPat(Ops.BIND, name="x"), do_unbind)])
# ctx is source UOp for which we are finding a contiguous view for. used in contiguous_view_offset
pm_contiguous_view_offset = PatternMatcher([
(UPat(Ops.INDEX, src=(UPat(),)), lambda: UOp.const(0)),
(UPat(Ops.INDEX, src=(UPat(), UPat(Ops.RANGE))), lambda: UOp.const(0)),
(UPat(Ops.INDEX, src=(UPat(), UPat(Ops.RANGE)+UPat.cvar('c'))), lambda c: c),
(UPat(Ops.INDEX, src=(UPat(), UPat.cvar('c'))), lambda ctx, c: c if resolve(ctx.numel() == 1, False) else None),
])
# *** what was symbolic.py ***
sint = int|UOp
Variable = UOp
ConstLike = ConstType|Variable|tuple[ConstType, ...]
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